Methods and systems for generating a supplement instruction set using artificial intelligence

ABSTRACT

A system for generating a supplement instruction set using artificial intelligence. The system includes at least a server wherein the at least a server is designed and configured to receive training data. The system includes a diagnostic engine operating on the at least a server designed and configured to record at least a biological extraction from a user and generate a diagnostic output based on the at least a biological extraction and training data. The system includes a plan generator module operating on the at least a server designed and configured to generate a comprehensive instruction set associated with the user as a function of the diagnostic output. The system includes a supplement plan generator module operating on the at least a server designed and configured to generate a supplement instruction set as a function of the comprehensive instruction set.

FIELD OF THE INVENTION

The present invention generally relates to the field of artificialintelligence. In particular, the present invention is directed tomethods and systems for generating a supplement instruction set usingartificial intelligence.

BACKGROUND

Currently, generation of instruction sets is a process challenged bycomplexity of data to be analyzed. Further, incorrect and/or inaccurateuse of such data can have potentially devastating results. Existingsolutions fail to combine useful information that is accurate and can berelied upon.

SUMMARY OF THE DISCLOSURE

A system for generating a supplement instruction set using artificialintelligence. The system includes at least a server, the at least aserver designed and configured to receive training data, whereinreceiving the training data further comprises receiving a first trainingset including a plurality of first data entries, each first data entryof the plurality of first data entries including at least an element ofphysiological state data and at least a correlated first prognosticlabel; and receiving a second training set including a plurality ofsecond data entries, each second data entry of the plurality of seconddata entries including at least a second prognostic label and at least acorrelated ameliorative process label. The system includes a diagnosticengine operating on the at least a server, wherein the diagnostic engineis configured to record at least a biological extraction from a user.The diagnostic engine operating on the at least a server is furtherconfigured to generate a diagnostic output based on the at least abiological extraction and the training data, wherein generating furthercomprises performing at least a machine-learning algorithm as a functionof the training data and the at least a biological extraction. Thesystem includes a plan generator module operating on the at least aserver, the plan generator module designed and configured to generate, acomprehensive instruction set associated with the user as a function ofthe diagnostic output. The system includes a supplement plan generationmodule operating on the at least a server, the supplement plangeneration module designed and configured to generate, a supplementinstruction set associated with the user as a function of thecomprehensive instruction set.

A method of generating a supplement instruction set using artificialintelligence, the method including receiving by at least a servertraining data, wherein receiving the training data further comprisesreceiving a first training set including a plurality of first dataentries, each first data entry of the plurality of first data entriesincluding at least an element of physiological state data and at least acorrelated first prognostic label; and receiving a second training setincluding a plurality of second data entries, each second data entry ofthe plurality of second data entries including at least a secondprognostic label and at least a correlated ameliorative process label.The method includes recording by the at least a server at least abiological extraction from a user. The method includes generating by theat least a server a diagnostic output based on the at least a biologicalextraction and the training data, wherein generating further comprisesperforming at least a machine-learning algorithm as a function of thetraining data and the at least a biological extraction. The methodincludes generating by the at least a server a comprehensive instructionset associated with the user as a function of the diagnostic output. Themethod includes generating by the at least a server a supplementinstruction set associated with the user as a function of thecomprehensive instruction set.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary embodiment of asystem for generating a supplement instruction set based on vibrantconstitutional guidance;

FIG. 2 is a block diagram illustrating embodiments of data storagefacilities for use in disclosed systems and methods;

FIG. 3 is a block diagram illustrating an exemplary embodiment of abiological extraction database;

FIG. 4 is a block diagram illustrating an exemplary embodiment of anexpert knowledge database;

FIG. 5 is a block diagram illustrating an exemplary embodiment of aprognostic label database;

FIG. 6 is a block diagram illustrating an exemplary embodiment of anameliorative process label database;

FIG. 7 is a block diagram illustrating an exemplary embodiment of aprognostic label learner and associated system elements;

FIG. 8 is a block diagram illustrating an exemplary embodiment of anameliorative process label learner and associated system elements;

FIG. 9 is a block diagram illustrating an exemplary embodiment of a plangenerator module and associated system elements;

FIG. 10 is a block diagram illustrating an exemplary embodiment of aprognostic label classification database;

FIG. 11 is a block diagram illustrating an exemplary embodiment of anameliorative process label classification database;

FIG. 12 is a block diagram illustrating an exemplary embodiment of anarrative language database;

FIG. 13 is a block diagram illustrating an exemplary embodiment of animage database;

FIG. 14 is a block diagram illustrating an exemplary embodiment of auser database;

FIG. 15 is a block diagram illustrating an exemplary embodiment of asupplement plan generator module and associated system elements;

FIG. 16 is a block diagram illustrating an exemplary embodiment of asupplement instruction descriptor learner;

FIG. 17 is a block diagram illustrating an exemplary embodiment of asupplement instruction descriptor database;

FIG. 18 is a block diagram illustrating an exemplary embodiment of anadvisory module and associated system elements;

FIG. 19 is a block diagram illustrating an exemplary embodiment of anartificial intelligence advisor and associated system elements;

FIG. 20 is a block diagram illustrating an exemplary embodiment of anadvisory database;

FIG. 21 is a block diagram illustrating an exemplary embodiment of adefault response database;

FIG. 22 is a flow diagram illustrating an exemplary embodiment of amethod of generating a supplement instruction set using artificialintelligence; and

FIG. 23 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present invention are directed tomethods and systems for generating a supplement instruction set usingartificial intelligence. Supplement instruction set may includerecommendations for a user to consume containing supplements generatedfrom a biological extraction and/or a diagnostic output. Supplementinstruction sets may be generated based on user data that may include auser preference. Supplement instruction sets may be generated based onadvisor data that may include input from an informed advisor. Generationof supplement instruction sets may be done utilizing machine learningprocesses including both supervised and unsupervised processes.

Turning now to FIG. 1, a system 100 for generating a supplementinstruction set based on vibrant constitutional guidance is illustrated.System 100 includes at least a server 102. At least a server 102 mayinclude any computing device as described herein, including withoutlimitation a microcontroller, microprocessor, digital signal processor(DSP) and/or system on a chip (SoC) as described herein. At least aserver 102 may be housed with, may be incorporated in, or mayincorporate one or more sensors of at least a sensor. Computing devicemay include, be included in, and/or communicate with a mobile devicesuch as a mobile telephone or smartphone. At least a server 102 mayinclude a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. At least a server 102 with one or more additional devices asdescribed below in further detail via a network interface device.Network interface device may be utilized for connecting a at least aserver 102 to one or more of a variety of networks, and one or moredevices. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device. Atleast a server 102 may include but is not limited to, for example, a atleast a server 102 or cluster of computing devices in a first locationand a second computing device or cluster of computing devices in asecond location. At least a server 102 may include one or more computingdevices dedicated to data storage, security, distribution of traffic forload balancing, and the like. At least a server 102 may distribute oneor more computing tasks as described below across a plurality ofcomputing devices of computing device, which may operate in parallel, inseries, redundantly, or in any other manner used for distribution oftasks or memory between computing devices. At least a server 102 may beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofsystem 100 and/or computing device.

Still referring to FIG. 1, system 100 includes a diagnostic engine 104operating on the at least a server 102, wherein the diagnostic engine104 configured to receive a first training set including a plurality offirst data entries, each first data entry of the plurality of first dataentries including at least an element of physiological state data and atleast a correlated first prognostic label; receive a second training setincluding a plurality of second data entries, each second data entry ofthe plurality of second data entries including at least a secondprognostic label and at least a correlated ameliorative process label;receive at least a biological extraction from a user; and generate adiagnostic output based on the at least a biological extraction, thediagnostic output including at least a prognostic label and at least anameliorative process label using the first training set, the secondtraining set, and the at least a biological extraction. At least aserver 102, diagnostic engine 104, and/or one or more modules operatingthereon may be designed and/or configured to perform any method, methodstep, or sequence of method steps in any embodiment described in thisdisclosure, in any order and with any degree of repetition. Forinstance, at least a server 102 and/or diagnostic engine 104 may beconfigured to perform a single step or sequence repeatedly until adesired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. At least a server 102and/or diagnostic engine 104 may perform any step or sequence of stepsas described in this disclosure in parallel, such as simultaneouslyand/or substantially simultaneously performing a step two or more timesusing two or more parallel threads, processor cores, or the like;division of tasks between parallel threads and/or processes may beperformed according to any protocol suitable for division of tasksbetween iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Continuing to refer to FIG. 1, diagnostic engine 104 may be designed andconfigured to receive training data. Training data, as used herein, isdata containing correlation that a machine-learning process may use tomodel relationships between two or more categories of data elements. Forinstance, and without limitation, training data may include a pluralityof data entries, each entry representing a set of data elements thatwere recorded, received, and/or generated together; data elements may becorrelated by shared existence in a given data entry, by proximity in agiven data entry, or the like. Multiple data entries in training datamay evince one or more trends in correlations between categories of dataelements; for instance, and without limitation, a higher value of afirst data element belonging to a first category of data element maytend to correlate to a higher value of a second data element belongingto a second category of data element, indicating a possible proportionalor other mathematical relationship linking values belonging to the twocategories. Multiple categories of data elements may be related intraining data according to various correlations; correlations mayindicate causative and/or predictive links between categories of dataelements, which may be modeled as relationships such as mathematicalrelationships by machine-learning processes as described in furtherdetail below. Training data may be formatted and/or organized bycategories of data elements, for instance by associating data elementswith one or more descriptors corresponding to categories of dataelements. As a non-limiting example, training data may include dataentered in standardized forms by persons or processes, such that entryof a given data element in a given field in a form may be mapped to oneor more descriptors of categories. Elements in training data may belinked to descriptors of categories by tags, tokens, or other dataelements; for instance, and without limitation, training data may beprovided in fixed-length formats, formats linking positions of data tocategories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),enabling processes or devices to detect categories of data.

Alternatively or additionally, and still referring to FIG. 1, trainingdata may include one or more elements that are not categorized; that is,training data may not be formatted or contain descriptors for someelements of data. Machine-learning algorithms and/or other processes maysort training data according to one or more categorizations using, forinstance, natural language processing algorithms, tokenization,detection of correlated values in raw data and the like; categories maybe generated using correlation and/or other processing algorithms. As anon-limiting example, in a corpus of text, phrases making up a number“n” of compound words, such as nouns modified by other nouns, may beidentified according to a statistically significant prevalence ofn-grams containing such words in a particular order; such an n-gram maybe categorized as an element of language such as a “word” to be trackedsimilarly to single words, generating a new category as a result ofstatistical analysis. Similarly, in a data entry including some textualdata, a person's name and/or a description of a medical condition ortherapy may be identified by reference to a list, dictionary, or othercompendium of terms, permitting ad-hoc categorization bymachine-learning algorithms, and/or automated association of data in thedata entry with descriptors or into a given format. The ability tocategorize data entries automatedly may enable the same training data tobe made applicable for two or more distinct machine-learning algorithmsas described in further detail below.

Still referring to FIG. 1, diagnostic engine 104 may be configured toreceive a first training set 106 including a plurality of first dataentries, each first data entry of the first training set 106 includingat least an element of physiological state data 108 and at least acorrelated first prognostic label 110. At least an element ofphysiological state data 108 may include any data indicative of aperson's physiological state; physiological state may be evaluated withregard to one or more measures of health of a person's body, one or moresystems within a person's body such as a circulatory system, a digestivesystem, a nervous system, or the like, one or more organs within aperson's body, and/or any other subdivision of a person's body usefulfor diagnostic or prognostic purposes. Physiological state data 108 mayinclude, without limitation, hematological data, such as red blood cellcount, which may include a total number of red blood cells in a person'sblood and/or in a blood sample, hemoglobin levels, hematocritrepresenting a percentage of blood in a person and/or sample that iscomposed of red blood cells, mean corpuscular volume, which may be anestimate of the average red blood cell size, mean corpuscularhemoglobin, which may measure average weight of hemoglobin per red bloodcell, mean corpuscular hemoglobin concentration, which may measure anaverage concentration of hemoglobin in red blood cells, platelet count,mean platelet volume which may measure the average size of platelets,red blood cell distribution width, which measures variation in red bloodcell size, absolute neutrophils, which measures the number of neutrophilwhite blood cells, absolute quantities of lymphocytes such as B-cells,T-cells, Natural Killer Cells, and the like, absolute numbers ofmonocytes including macrophage precursors, absolute numbers ofeosinophils, and/or absolute counts of basophils. Physiological statedata 108 may include, without limitation, immune function data such asInterleukine-6 (IL-6), TNF-alpha, systemic inflammatory cytokines, andthe like.

Continuing to refer to FIG. 1, physiological state data 108 may include,without limitation, data describing blood-born lipids, including totalcholesterol levels, high-density lipoprotein (HDL) cholesterol levels,low-density lipoprotein (LDL) cholesterol levels, very low-densitylipoprotein (VLDL) cholesterol levels, levels of triglycerides, and/orany other quantity of any blood-born lipid or lipid-containingsubstance. Physiological state data 108 may include measures of glucosemetabolism such as fasting glucose levels and/or hemoglobin A1-C (HbA1c)levels. Physiological state data 108 may include, without limitation,one or more measures associated with endocrine function, such as withoutlimitation, quantities of dehydroepiandrosterone (DHEAS), DHEA-Sulfate,quantities of cortisol, ratio of DHEAS to cortisol, quantities oftestosterone quantities of estrogen, quantities of growth hormone (GH),insulin-like growth factor 1 (IGF-1), quantities of adipokines such asadiponectin, leptin, and/or ghrelin, quantities of somatostatin,progesterone, or the like. Physiological state data 108 may includemeasures of estimated glomerular filtration rate (eGFR). Physiologicalstate data 108 may include quantities of C-reactive protein, estradiol,ferritin, folate, homocysteine, prostate-specific Ag,thyroid-stimulating hormone, vitamin D, 25 hydroxy, blood urea nitrogen,creatinine, sodium, potassium, chloride, carbon dioxide, uric acid,albumin, globulin, calcium, phosphorus, alkaline phosphatase, alanineamino transferase, aspartate amino transferase, lactate dehydrogenase(LDH), bilirubin, gamma-glutamyl transferase (GGT), iron, and/or totaliron binding capacity (TIBC), or the like. Physiological state data 108may include antinuclear antibody levels. Physiological state data 108may include aluminum levels. Physiological state data 108 may includearsenic levels. Physiological state data 108 may include levels offibrinogen, plasma cystatin C, and/or brain natriuretic peptide.

Continuing to refer to FIG. 1, physiological state data 108 may includemeasures of lung function such as forced expiratory volume, one second(FEV-1) which measures how much air can be exhaled in one secondfollowing a deep inhalation, forced vital capacity (FVC), which measuresthe volume of air that may be contained in the lungs. Physiologicalstate data 108 may include a measurement blood pressure, includingwithout limitation systolic and diastolic blood pressure. Physiologicalstate data 108 may include a measure of waist circumference.Physiological state data 108 may include body mass index (BMI).Physiological state data 108 may include one or more measures of bonemass and/or density such as dual-energy x-ray absorptiometry.Physiological state data 108 may include one or more measures of musclemass. Physiological state data 108 may include one or more measures ofphysical capability such as without limitation measures of gripstrength, evaluations of standing balance, evaluations of gait speed,pegboard tests, timed up and go tests, and/or chair rising tests.

Still viewing FIG. 1, physiological state data 108 may include one ormore measures of cognitive function, including without limitation Reyauditory verbal learning test results, California verbal learning testresults, NIH toolbox picture sequence memory test, Digital symbol codingevaluations, and/or Verbal fluency evaluations. Physiological state data204 may include one or more measures of psychological function or state,such as without limitation clinical interviews, assessments ofintellectual functioning and/or intelligence quotient (IQ) tests,personality assessments, and/or behavioral assessments. Physiologicalstate data 204 may include one or more psychological self-assessments,which may include any self-administered and/or automatedlycomputer-administered assessments, whether administered within system100 and/or via a third-party service or platform.

Continuing to refer to FIG. 1, physiological state data may includepsychological data. Psychological data may include any data generatedusing psychological, neuro-psychological, and/or cognitive evaluations,as well as diagnostic screening tests, personality tests, personalcompatibility tests, or the like; such data may include, withoutlimitation, numerical score data entered by an evaluating professionaland/or by a subject performing a self-test such as a computerizedquestionnaire. Psychological data may include textual, video, or imagedata describing testing, analysis, and/or conclusions entered by amedical professional such as without limitation a psychologist,psychiatrist, psychotherapist, social worker, a medical doctor, or thelike. Psychological data may include data gathered from userinteractions with persons, documents, and/or computing devices; forinstance, user patterns of purchases, including electronic purchases,communication such as via chat-rooms or the like, any textual, image,video, and/or data produced by the subject, any textual image, videoand/or other data depicting and/or describing the subject, or the like.Any psychological data and/or data used to generate psychological datamay be analyzed using machine-learning and/or language processingmodules as described in this disclosure.

With continued reference to FIG. 1, physiological state data 108 mayinclude one or more evaluations of sensory ability, including measuresof audition, vision, olfaction, gustation, vestibular function and pain.Physiological state data 108 may include genomic data, includingdeoxyribonucleic acid (DNA) samples and/or sequences, such as withoutlimitation DNA sequences contained in one or more chromosomes in humancells. Genomic data may include, without limitation, ribonucleic acid(RNA) samples and/or sequences, such as samples and/or sequences ofmessenger RNA (mRNA) or the like taken from human cells. Genetic datamay include telomere lengths. Genomic data may include epigenetic dataincluding data describing one or more states of methylation of geneticmaterial. Physiological state data 108 may include proteomic data, whichas used herein, is data describing all proteins produced and/or modifiedby an organism, colony of organisms, or system of organisms, and/or asubset thereof. Physiological state data 108 may include data concerninga microbiome of a person, which as used herein, includes any datadescribing any microorganism and/or combination of microorganisms livingon or within a person, including without limitation biomarkers, genomicdata, proteomic data, and/or any other metabolic or biochemical datauseful for analysis of the effect of such microorganisms on otherphysiological state data 108 of a person, and/or on prognostic labelsand/or ameliorative processes as described in further detail below.Physiological state data 108 may include any physiological state data108, as described above, describing any multicellular organism living inor on a person including any parasitic an

symbiotic organisms living in or on the persons; non-limiting examplesmay include mites, nematodes, flatworms, or the like.

With continuing reference to FIG. 1, physiological state data mayinclude one or more user-entered descriptions of a person'sphysiological state. One or more user-entered descriptions may include,without limitation, user descriptions of symptoms, which may includewithout limitation current or past physical, psychological, perceptual,and/or neurological symptoms, user descriptions of current or pastphysical, emotional, and/or psychological problems and/or concerns, userdescriptions of past or current treatments, including therapies,nutritional regimens, exercise regimens, pharmaceuticals or the like, orany other user-entered data that a user may provide to a medicalprofessional when seeking treatment and/or evaluation, and/or inresponse to medical intake papers, questionnaires, questions frommedical professionals, or the like. Examples of physiological state data108 described in this disclosure are presented for illustrative purposesonly and are not meant to be exhaustive. Persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variousadditional examples of physiological state data 108 that may be usedconsistently with descriptions of systems and methods as provided inthis disclosure.

Continuing to refer to FIG. 1, each element of first training set 106includes at least a first prognostic label 110. A prognostic label, asdescribed herein, is an element of data identifying and/or describing acurrent, incipient, or probable future medical condition affecting aperson; medical condition may include a particular disease, one or moresymptoms associated with a syndrome, a syndrome, and/or any othermeasure of current or future health and/or healthy aging. At least aprognostic label may be associated with a physical and/or somaticcondition, a mental condition such as a mental illness, neurosis, or thelike, or any other condition affecting human health that may beassociated with one or more elements of physiological state data 108 asdescribed in further detail below. Conditions associated with prognosticlabels may include, without limitation one or more diseases, defined forpurposes herein as conditions that negatively affect structure and/orfunction of part or all of an organism. Conditions associated withprognostic labels may include, without limitation, acute or chronicinfections, including without limitation infections by bacteria,archaea, viruses, viroids, prions, single-celled eukaryotic organismssuch as amoeba, paramecia, trypanosomes, plasmodia, leishmania, and/orfungi, and/or multicellular parasites such as nematodes, arthropods,fungi, or the like. Prognostic labels may be associated with one or moreimmune disorders, including without limitation immunodeficiencies and/orauto-immune conditions. Prognostic labels may be associated with one ormore metabolic disorders. Prognostic labels may be associated with oneor more endocrinal disorders. Prognostic labels may be associated withone or more cardiovascular disorders. Prognostic labels may beassociated with one or more respiratory disorders. P

nostic labels may be associated with one or more disorders affectingconnective tissue. Prognostic labels may be associated with one or moredigestive disorders. Prognostic labels may be associated with one ormore neurological disorders such as neuromuscular disorders, dementia,or the like. Prognostic labels may be associated with one or moredisorders of the excretory system, including without limitationnephrological disorders. Prognostic labels may be associated with one ormore liver disorders. Prognostic labels may be associated with one ormore disorders of the bones such as osteoporosis. Prognostic labels maybe associated with one or more disorders affecting joints, such asosteoarthritis, gout, and/or rheumatoid arthritis. Prognostic labels beassociated with one or more cancers, including without limitationcarcinomas, lymphomas, leukemias, germ cell tumor cancers, blastomas,and/or sarcomas. Prognostic labels may include descriptors of latent,dormant, and/or apparent disorders, diseases, and/or conditions.Prognostic labels may include descriptors of conditions for which aperson may have a higher than average probability of development, suchas a condition for which a person may have a “risk factor”; forinstance, a person currently suffering from abdominal obesity may have ahigher than average probability of developing type II diabetes. Theabove-described examples are presented for illustrative purposes onlyand are not intended to be exhaustive. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousadditional examples of conditions that may be associated with prognosticlabels as described in this disclosure.

Still referring to FIG. 1, at least a prognostic label may be stored inany suitable data and/or data type. For instance, and withoutlimitation, at least a prognostic label may include textual data, suchas numerical, character, and/or string data. Textual data may include astandardized name and/or code for a disease, disorder, or the like;codes may include diagnostic codes and/or diagnosis codes, which mayinclude without limitation codes used in diagnosis classificationsystems such as The International Statistical Classification of Diseasesand Related Health Problems (ICD). In general, there is no limitation onforms textual data or non-textual data used as at least a prognosticlabel may take; persons skilled in the art, upon reviewing the entiretyof this disclosure, will be aware of various forms which may be suitablefor use as at least a prognostic label consistently with thisdisclosure.

With continued reference to FIG. 1, in each first data element of firsttraining set 106, at least a first prognostic label 110 of the dataelement is correlated with at least an element of physiological statedata 108 of the data element. In an embodiment, an element ofphysiological data is correlated with a prognostic label where theelement of physiological data is located in the same data element and/orportion of data element as the prognostic label; for example, andwithout limitation, an element of physiological data is correlated witha prognostic element where both element of physiological data andprognostic element are contained within the same first data element ofthe first training set 106. As a further example, an element ofphysiological data is correlated with a prognostic element where bothshare a category label as described in further detail below, where eachis within a certain distance of the other within an ordered collectionof data in data element, or the like. Still further, an element ofphysiological data may be correlated with a prognostic label where theelement of physiological data and the prognostic label share an origin,such as being data that was collected with regard to a single person orthe like. In an embodiment, a first datum may be more closely correlatedwith a second datum in the same data element than with a third datumcontained in the same data element; for instance, the first element andthe second element may be closer to each other in an ordered set of datathan either is to the third element, the first element and secondelement may be contained in the same subdivision and/or section of datawhile the third element is in a different subdivision and/or section ofdata, or the like. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various forms and/ordegrees of correlation between physiological data and prognostic labelsthat may exist in first training set 106 and/or first data elementconsistently with this disclosure.

In an embodiment, and still referring to FIG. 1, diagnostic engine 104may be designed and configured to associate at least an element ofphysiological state data 108 with at least a category from a list ofsignificant categories of physiological state data 108. Significantcategories of physiological state data 108 may include labels and/ordescriptors describing types of physiological state data 108 that areidentified as being of high relevance in identifying prognostic labels.As a non-limiting example, one or more categories may identifysignificant categories of physiological state data 108 based on degreeof diagnostic relevance to one or more impactful conditions and/orwithin one or more medical or public health fields. For instance, andwithout limitation, a particular set of biomarkers, test results, and/orbiochemical information may be recognized in a given medical field asuseful for identifying various disease conditions or prognoses within arelevant field. As a non-limiting example, and without limitation,physiological data describing red blood cells, such as red blood cellcount, hemoglobin levels, hematocrit, mean corpuscular volume, meancorpuscular hemoglobin, and/or mean corpuscular hemoglobin concentrationmay be recognized as useful for identifying various conditions such asdehydration, high testosterone, nutrient deficiencies, kidneydysfunction, chronic inflammation, anemia, and/or blood loss. As anadditional example, hemoglobin levels may be useful for identifyingelevated testosterone, poor oxygen deliverability, thiamin deficiency,insulin resistance, anemia, liver disease, hypothyroidism, argininedeficiency, protein deficiency, inflammation, and/or nutrientdeficiencies. In a further non-limiting example, hematocrit may beuseful for identifying dehydration, elevated testosterone, poor oxygendeliverability, thiamin deficiency, insulin resistance, anemia, liverdisease, hypothyroidism, arginine deficiency, protein deficiency,inflammation, and/or nutrient deficiencies. Similarly, measures of lipidlevels in blood, such as total cholesterol, HDL, LDL, VLDL,triglycerides, LDL-C and/or HDL-C may be recognized as useful inidentifying conditions such as poor thyroid function, insulinresistance, blood glucose dysregulation, magnesium deficiency,dehydration, kidney disease, familial hypercholesterolemia, liverdysfunction, oxidative stress, inflammation, malabsorption, anemia,alcohol abuse, diabetes, hypercholesterolemia, coronary artery disease,atherosclerosis, or the like. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various additionalcategories of physiological data that may be used consistently with thisdisclosure.

Still referring to FIG. 1, diagnostic engine 104 may receive the list ofsignificant categories according to any suitable process; for instance,and without limitation, diagnostic engine 104 may receive the list ofsignificant categories from at least an expert. In an embodiment,diagnostic engine 104 and/or a user device connected to diagnosticengine 104 may provide a graphical user interface, which may includewithout limitation a form or other graphical element having data entryfields, wherein one or more experts, including without limitationclinical and/or scientific experts, may enter information describing oneor more categories of physiological data that the experts consider to besignificant or useful for detection of conditions; fields in graphicaluser interface may provide options describing previously identifiedcategories, which may include a comprehensive or near-comprehensive listof types of physiological data detectable using known or recordedtesting methods, for instance in “drop-down” lists, where experts may beable to select one or more entries to indicate their usefulness and/orsignificance in the opinion of the experts. Fields may include free-formentry fields such as text-entry fields where an expert may be able totype or otherwise enter text, enabling expert to propose or suggestcategories not currently recorded. Graphical user interface or the likemay include fields corresponding to prognostic labels, where experts mayenter data describing prognostic labels and/or categories of prognosticlabels the experts consider related to entered categories ofphysiological data; for instance, such fields may include drop-downlists or other pre-populated data entry fields listing currentlyrecorded prognostic labels, and which may be comprehensive, permittingeach expert to select a prognostic label and/or a plurality ofprognostic labels the expert believes to be predicted and/or associatedwith each category of physiological data selected by the expert. Fieldsfor entry of prognostic labels and/or categories of prognostic labelsmay include free-form data entry fields such as text entry fields; asdescribed above, examiners may enter data not presented in pre-populateddata fields in the free-form data entry fields. Alternatively oradditionally, fields for entry of prognostic labels may enable an expertto select and/or enter information describing or linked to a category ofprognostic label that the expert considers significant, wheresignificance may indicate likely impact on longevity, mortality, qualityof life, or the like as described in further detail below. Graphicaluser interface may provide an expert with a field in which to indicate areference to a document describing significant categories ofphysiological data, relationships of such categories to prognosticlabels, and/or significant categories of prognostic labels. Any datadescribed above may alternatively or additionally be received fromexperts similarly organized in paper form, which may be captured andentered into data in a similar way, or in a textual form such as aportable document file (PDF) with examiner entries, or the like

Still referring to FIG. 1, diagnostic engine 104 may receive the list ofsignificant categories according to any suitable process; for instance,and without limitation, diagnostic engine 104 may receive the list ofsignificant categories from at least an expert. In an embodiment,diagnostic engine 104 and/or a user device connected to diagnosticengine 104 may provide a graphical user interface 112, which may includewithout limitation a form or other graphical element having data entryfields, wherein one or more experts, including without limitationclinical and/or scientific experts, may enter information describing oneor more categories of physiological data that the experts consider to besignificant or useful for detection of conditions; fields in graphicaluser interface may provide options describing previously identifiedcategories, which may include a comprehensive or near-comprehensive listof types of physiological data detectable using known or recordedtesting methods, for instance in “drop-down” lists, where experts may beable to select one or more entries to indicate their usefulness and/orsignificance in the opinion of the experts. Fields may include free-formentry fields such as text-entry fields where an expert may be able totype or otherwise enter text, enabling expert to propose or suggestcategories not currently recorded. Graphical user interface 112 or thelike may include fields corresponding to prognostic labels, whereexperts may enter data describing prognostic labels and/or categories ofprognostic labels the experts consider related to entered categories ofphysiological data; for instance, such fields may include drop-downlists or other pre-populated data entry fields listing currentlyrecorded prognostic labels, and which may be comprehensive, permittingeach expert to select a prognostic label and/or a plurality ofprognostic labels the expert believes to be predicted and/or associatedwith each category of physiological data selected by the expert. Fieldsfor entry of prognostic labels and/or categories of prognostic labelsmay include free-form data entry fields such as text entry fields; asdescribed above, examiners may enter data not presented in pre-populateddata fields in the free-form data entry fields. Alternatively oradditionally, fields for entry of prognostic labels may enable an expertto select and/or enter information describing or linked to a category ofprognostic label that the expert considers significant, wheresignificance may indicate likely impact on longevity, mortality, qualityof life, or the like as described in further detail below. Graphicaluser interface 112 may provide an expert with a field in which toindicate a reference to a document describing significant categories ofphysiological data, relationships of such categories to prognosticlabels, and/or significant categories of prognostic labels. Any datadescribed above may alternatively or additionally be received fromexperts similarly organized in paper form, which may be captured andentered into data in a similar way, or in a textual form such as aportable document file (PDF) with examiner entries, or the like

With continued reference to FIG. 1, data information describingsignificant categories of physiological data, relationships of suchcategories to prognostic labels, and/or significant categories ofprognostic labels may alternatively or additionally be extracted fromone or more documents using a language processing module 114. Languageprocessing module 114 may include any hardware and/or software module.Language processing module 114 may be configured to extract, from theone or more documents, one or more words. One or more words may include,without limitation, strings of one or characters, including withoutlimitation any sequence or sequences of letters, numbers, punctuation,diacritic marks, engineering symbols, geometric dimensioning andtolerancing (GD&T) symbols, chemical symbols and formulas, spaces,whitespace, and other symbols, including any symbols usable as textualdata as described above. Textual data may be parsed into tokens, whichmay include a simple word (sequence of letters separated by whitespace)or more generally a sequence of characters as described previously. Theterm “token,” as used herein, refers to any smaller, individualgroupings of text from a larger source of text; tokens may be broken upby word, pair of words, sentence, or other delimitation. These tokensmay in turn be parsed in various ways. Textual data may be parsed intowords or sequences of words, which may be considered words as well.Textual data may be parsed into “n-grams”, where all sequences of nconsecutive characters are considered. Any or all possible sequences oftokens or words may be stored as “chains”, for example for use as aMarkov chain or Hidden Markov Model.

Still referring to FIG. 1, language processing module 114 may compareextracted words to categories of physiological data recorded atdiagnostic engine 104, one or more prognostic labels recorded atdiagnostic engine 104, and/or one or more categories of prognosticlabels recorded at diagnostic engine 104; such data for comparison maybe entered on diagnostic engine 104 as described above using expert datainputs or the like. In an embodiment, one or more categories may beenumerated, to find total count of mentions in such documents.Alternatively or additionally, language processing module 114 mayoperate to produce a language processing model. Language processingmodel may include a program automatically generated by diagnostic engine104 and/or language processing module 114 to produce associationsbetween one or more words extracted from at least a document and detectassociations, including without limitation mathematical associations,between such words, and/or associations of extracted words withcategories of physiological data, relationships of such categories toprognostic labels, and/or categories of prognostic labels. Associationsbetween language elements, where language elements include for purposesherein extracted words, categories of physiological data, relationshipsof such categories to prognostic labels, and/or categories of prognosticlabels may include, without limitation, mathematical associations,including without limitation statistical correlations between anylanguage element and any other language element and/or languageelements. Statistical correlations and/or mathematical associations mayinclude probabilistic formulas or relationships indicating, forinstance, a likelihood that a given extracted word indicates a givencategory of physiological data, a given relationship of such categoriesto prognostic labels, and/or a given category of prognostic labels. As afurther example, statistical correlations and/or mathematicalassociations may include probabilistic formulas or relationshipsindicating a positive and/or negative association between at least anextracted word and/or a given category of physiological data, a givenrelationship of such categories to prognostic labels, and/or a givencategory of prognostic labels; positive or negative indication mayinclude an indication that a given document is or is not indicating acategory of physiological data, relationship of such category toprognostic labels, and/or category of prognostic labels is or is notsignificant. For instance, and without limitation, a negative indicationmay be determined from a phrase such as “telomere length was not foundto be an accurate predictor of overall longevity,” whereas a positiveindication may be determined from a phrase such as “telomere length wasfound to be an accurate predictor of dementia,” as an illustrativeexample; whether a phrase, sentence, word, or other textual element in adocument or corpus of documents constitutes a positive or negativeindicator may be determined, in an embodiment, by mathematicalassociations between detected words, comparisons to phrases and/or wordsindicating positive and/or negative indicators that are stored in memoryat diagnostic engine 104, or the like.

Still referring to FIG. 1, language processing module 114 and/ordiagnostic engine 104 may generate the language processing model by anysuitable method, including without limitation a natural languageprocessing classification algorithm; language processing model mayinclude a natural language process classification model that enumeratesand/or derives statistical relationships between input term and outputterms. Algorithm to generate language processing model may include astochastic gradient descent algorithm, which may include a method thatiteratively optimizes an objective function, such as an objectivefunction representing a statistical estimation of relationships betweenterms, including relationships between input terms and output terms, inthe form of a sum of relationships to be estimated. In an alternative oradditional approach, sequential tokens may be modeled as chains, servingas the observations in a Hidden Markov Model (HMM). HMMs as used herein,are statistical models with inference algorithms that that may beapplied to the models. In such models, a hidden state to be estimatedmay include an association between an extracted word category ofphysiological data, a given relationship of such categories toprognostic labels, and/or a given category of prognostic labels. Theremay be a finite number of category of physiological data, a givenrelationship of such categories to prognostic labels, and/or a givencategory of prognostic labels to which an extracted word may pertain; anHMM inference algorithm, such as the forward-backward algorithm or theViterbi algorithm, may be used to estimate the most likely discretestate given a word or sequence of words. Language processing module 114may combine two or more approaches. For instance, and withoutlimitation, machine-learning program may use a combination ofNaive-Bayes (NB), Stochastic Gradient Descent (SGD), and parametergrid-searching classification techniques; the result may include aclassification algorithm that returns ranked associations.

Continuing to refer to FIG. 1, generating language processing model mayinclude generating a vector space, which may be a collection of vectors,defined as a set of mathematical objects that can be added togetherunder an operation of addition following properties of associativity,commutativity, existence of an identity element, and existence of aninverse element for each vector, and can be multiplied by scalar valuesunder an operation of scalar multiplication compatible with fieldmultiplication, and that has an identity element is distributive withrespect to vector addition, and is distributive with respect to fieldaddition. Each vector in an n-dimensional vector space may berepresented by an n-tuple of numerical values. Each unique extractedword and/or language element as described above may be represented by avector of the vector space. In an embodiment, each unique extractedand/or other language element may be represented by a dimension ofvector space; as a non-limiting example, each element of a vector mayinclude a number representing an enumeration of co-occurrences of theword and/or language element represented by the vector with another wordand/or language element. Vectors may be normalized, scaled according torelative frequencies of appearance and/or file sizes. In an embodimentassociating language elements to one another as described above mayinclude computing a degree of vector similarity between a vectorrepresenting each language element and a vector representing anotherlanguage element; vector similarity may be measured according to anynorm for proximity and/or similarity of two vectors, including withoutlimitation cosine similarity, which measures the similarity of twovectors by evaluating the cosine of the angle between the vectors, whichcan be computed using a dot product of the two vectors divided by thelengths of the two vectors. Degree of similarity may include any othergeometric measure of distance between vectors.

Still referring to FIG. 1, language processing module 114 may use acorpus of documents to generate associations between language elementsin a language processing module 114, and diagnostic engine 104 may thenuse such associations to analyze words extracted from one or moredocuments and determine that the one or more documents indicatesignificance of a category of physiological data, a given relationshipof such categories to prognostic labels, and/or a given category ofprognostic labels. In an embodiment, diagnostic engine 104 may performthis analysis using a selected set of significant documents, such asdocuments identified by one or more experts as representing goodscience, good clinical analysis, or the like; experts may identify orenter such documents via graphical user interface as described above inreference to FIG. 9, or may communicate identities of significantdocuments according to any other suitable method of electroniccommunication, or by providing such identity to other persons who mayenter such identifications into diagnostic engine 104. Documents may beentered into diagnostic engine 104 by being uploaded by an expert orother persons using, without limitation, file transfer protocol (FTP) orother suitable methods for transmission and/or upload of documents;alternatively or additionally, where a document is identified by acitation, a uniform resource identifier (URI), uniform resource locator(URL) or other datum permitting unambiguous identification of thedocument, diagnostic engine 104 may automatically obtain the documentusing such an identifier, for instance by submitting a request to adatabase or compendium of documents such as JSTOR as provided by IthakaHarbors, Inc. of New York.

Continuing to refer to FIG. 1, whether an entry indicating significanceof a category of physiological data, a given relationship of suchcategories to prognostic labels, and/or a given category of prognosticlabels is entered via graphical user interface, alternative submissionmeans, and/or extracted from a document or body of documents asdescribed above, an entry or entries may be aggregated to indicate anoverall degree of significance. For instance, each category ofphysiological data, relationship of such categories to prognosticlabels, and/or category of prognostic labels may be given an overallsignificance score; overall significance score may, for instance, beincremented each time an expert submission and/or paper indicatessignificance as described above. Persons skilled in the art, uponreviewing the entirety of this disclosure will be aware of other ways inwhich scores may be generated using a plurality of entries, includingaveraging, weighted averaging, normalization, and the like. Significancescores may be ranked; that is, all categories of physiological data,relationships of such categories to prognostic labels, and/or categoriesof prognostic labels may be ranked according significance scores, forinstance by ranking categories of physiological data, relationships ofsuch categories to prognostic labels, and/or categories of prognosticlabels higher according to higher significance scores and loweraccording to lower significance scores. Categories of physiologicaldata, relationships of such categories to prognostic labels, and/orcategories of prognostic labels may be eliminated from current use ifthey fail a threshold comparison, which may include a comparison ofsignificance score to a threshold number, a requirement thatsignificance score belong to a given portion of ranking such as athreshold percentile, quartile, or number of top-ranked scores.Significance scores may be used to filter outputs as described infurther detail below; for instance, where a number of outputs aregenerated and automated selection of a smaller number of outputs isdesired, outputs corresponding to higher significance scores may beidentified as more probable and/or selected for presentation while otheroutputs corresponding to lower significance scores may be eliminated.Alternatively or additionally, significance scores may be calculated persample type; for instance, entries by experts, documents, and/ordescriptions of purposes of a given type of physiological test or samplecollection as described above may indicate that for that type ofphysiological test or sample collection a first category ofphysiological data, relationship of such category to prognostic labels,and/or category of prognostic labels is significant with regard to thattest, while a second category of physiological data, relationship ofsuch category to prognostic labels, and/or category of prognostic labelsis not significant; such indications may be used to perform asignificance score for each category of physiological data, relationshipof such category to prognostic labels, and/or category of prognosticlabels is or is not significant per type of physiological sample, whichthen may be subjected to ranking, comparison to thresholds and/orelimination as described above.

Still referring to FIG. 1, diagnostic engine 104 may detect furthersignificant categories of physiological data, relationships of suchcategories to prognostic labels, and/or categories of prognostic labelsusing machine-learning processes, including without limitationunsupervised machine-learning processes as described in further detailbelow; such newly identified categories, as well as categories enteredby experts in free-form fields as described above, may be added topre-populated lists of categories, lists used to identify languageelements for language learning module, and/or lists used to identifyand/or score categories detected in documents, as described above.

Continuing to refer to FIG. 1, in an embodiment, diagnostic engine 104may be configured, for instance as part of receiving the first trainingset 106, to associate at least correlated first prognostic label 110with at least a category from a list of significant categories ofprognostic labels. Significant categories of prognostic labels may beacquired, determined, and/or ranked as described above. As anon-limiting example, prognostic labels may be organized according torelevance to and/or association with a list of significant conditions. Alist of significant conditions may include, without limitation,conditions having generally acknowledged impact on longevity and/orquality of life; this may be determined, as a non-limiting example, by aproduct of relative frequency of a condition within the population withyears of life and/or years of able-bodied existence lost, on average, asa result of the condition. A list of conditions may be modified for agiven person to reflect a family history of the person; for instance, aperson with a significant family history of a particular condition orset of conditions, or a genetic profile having a similarly significantassociation therewith, may have a higher probability of developing suchconditions than a typical person from the general population, and as aresult diagnostic engine 104 may modify list of significant categoriesto reflect this difference.

Still referring to FIG. 1, diagnostic engine 104 is designed andconfigured to receive a second training set 116 including a plurality ofsecond data entries. Each second data entry of the second training set116 includes at least a second prognostic label 118; at least a secondprognostic label 118 may include any label suitable for use as at leasta first prognostic label 110 as described above. Each second data entryof the second training set 116 includes at least an ameliorative processlabel 120 correlated with the at least a second prognostic label 118,where correlation may include any correlation suitable for correlationof at least a first prognostic label 110 to at least an element ofphysiological data as described above. As used herein, an ameliorativeprocess label 120 is an identifier, which may include any form ofidentifier suitable for use as a prognostic label as described above,identifying a process that tends to improve a physical condition of auser, where a physical condition of a user may include, withoutlimitation, any physical condition identifiable using a prognosticlabel. Ameliorative processes may include, without limitation, exerciseprograms, including amount, intensity, and/or types of exerciserecommended. Ameliorative processes may include, without limitation,dietary or nutritional recommendations based on data includingnutritional content, digestibility, or the like. Ameliorative processesmay include one or more medical procedures. Ameliorative processes mayinclude one or more physical, psychological, or other therapies.Ameliorative processes may include one or more medications. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various processes that may be used as ameliorative processesconsistently with this disclosure.

Continuing to refer to FIG. 1, in an embodiment diagnostic engine 104may be configured, for instance as part of receiving second training set116, to associate the at least second prognostic label 118 with at leasta category from a list of significant categories of prognostic labels.This may be performed as described above for use of lists of significantcategories with regard to at least a first prognostic label 110.Significance may be determined, and/or association with at least acategory, may be performed for prognostic labels in first training set106 according to a first process as described above and for prognosticlabels in second training set 116 according to a second process asdescribed above.

Still referring to FIG. 1, diagnostic engine 104 may be configured, forinstance as part of receiving second training set 116, to associate atleast a correlated ameliorative process label 120 with at least acategory from a list of significant categories of ameliorative processlabels 120. In an embodiment, diagnostic engine 104 and/or a user deviceconnected to diagnostic engine 104 may provide a second graphical userinterface 122 which may include without limitation a form or othergraphical element having data entry fields, wherein one or more experts,including without limitation clinical and/or scientific experts, mayenter information describing one or more categories of prognostic labelsthat the experts consider to be significant as described above; fieldsin graphical user interface may provide options describing previouslyidentified categories, which may include a comprehensive ornear-comprehensive list of types of prognostic labels, for instance in“drop-down” lists, where experts may be able to select one or moreentries to indicate their usefulness and/or significance in the opinionof the experts. Fields may include free-form entry fields such astext-entry fields where an expert may be able to type or otherwise entertext, enabling expert to propose or suggest categories not currentlyrecorded. Graphical user interface or the like may include fieldscorresponding to ameliorative labels, where experts may enter datadescribing ameliorative labels and/or categories of ameliorative labelsthe experts consider related to entered categories of prognostic labels;for instance, such fields may include drop-down lists or otherpre-populated data entry fields listing currently recorded ameliorativelabels, and which may be comprehensive, permitting each expert to selectan ameliorative label and/or a plurality of ameliorative labels theexpert believes to be predicted and/or associated with each category ofprognostic labels selected by the expert. Fields for entry ofameliorative labels and/or categories of ameliorative labels may includefree-form data entry fields such as text entry fields; as describedabove, examiners may enter data not presented in pre-populated datafields in the free-form data entry fields. Alternatively oradditionally, fields for entry of ameliorative labels may enable anexpert to select and/or enter information describing or linked to acategory of ameliorative label that the expert considers significant,where significance may indicate likely impact on longevity, mortality,quality of life, or the like as described in further detail below.Graphical user interface may provide an expert with a field in which toindicate a reference to a document describing significant categories ofprognostic labels, relationships of such categories to ameliorativelabels, and/or significant categories of ameliorative labels. Suchinformation may alternatively be entered according to any other suitablemeans for entry of expert data as described above. Data concerningsignificant categories of prognostic labels, relationships of suchcategories to ameliorative labels, and/or significant categories ofameliorative labels may be entered using analysis of documents usinglanguage processing module 114 or the like as described above.

In an embodiment, and still referring to FIG. 1, diagnostic engine 104may extract at least a second data entry from one or more documents;extraction may be performed using any language processing method asdescribed above. Diagnostic engine 104 may be configured, for instanceas part of receiving second training set 116, to receive at least adocument describing at least a medical history and extract at least asecond data entry of plurality of second data entries from the at leasta document. A medical history document may include, for instance, adocument received from an expert and/or medical practitioner describingtreatment of a patient; document may be anonymized by removal of one ormore patient-identifying features from document. A medical historydocument may include a case study, such as a case study published in amedical journal or written up by an expert. A medical history documentmay contain data describing and/or described by a prognostic label; forinstance, the medical history document may list a diagnosis that amedical practitioner made concerning the patient, a finding that thepatient is at risk for a given condition and/or evinces some precursorstate for the condition, or the like. A medical history document maycontain data describing and/or described by an ameliorative processlabel 120; for instance, the medical history document may list atherapy, recommendation, or other ameliorative process that a medicalpractitioner described or recommended to a patient. A medical historydocument may describe an outcome; for instance, medical history documentmay describe an improvement in a condition describing or described by aprognostic label, and/or may describe that the condition did notimprove. Prognostic labels, ameliorative process labels 120, and/orefficacy of ameliorative process labels 120 may be extracted from and/ordetermined from one or more medical history documents using anyprocesses for language processing as described above; for instance,language processing module 114 may perform such processes. As anon-limiting example, positive and/or negative indications regardingameliorative processes identified in medical history documents may bedetermined in a manner described above for determination of positiveand/or negative indications regarding categories of physiological data,relationships of such categories to prognostic labels, and/or categoriesof prognostic labels.

With continued reference to FIG. 1, diagnostic engine 104 may beconfigured, for instance as part of receiving second training set 116,to receiving at least a second data entry of the plurality of seconddata entries from at least an expert. This may be performed, withoutlimitation using second graphical user interface as described above.

Continuing to refer to FIG. 1, diagnostic engine 104 may be configuredto record at least a biological extraction. At least a biologicalextraction may include any element and/or elements of data suitable foruse as at least an element of physiological state data as describedabove. At least a biological extraction may include a physicallyextracted sample, which as used herein, includes a sample obtained byremoving and analyzing tissue and/or fluid. Physically extracted samplemay include without limitation a blood sample, a tissue sample, a buccalswab, a mucous sample, a stool sample, a hair sample, a fingernailsample, or the like. Physically extracted sample may include, as anon-limiting example, at least a blood sample. As a further non-limitingexample, at least a biological extraction may include at least a geneticsample. At least a genetic sample may include a complete genome of aperson or any portion thereof. At least a genetic sample may include aDNA sample and/or an RNA sample. At least a biological extraction mayinclude an epigenetic sample, a proteomic sample, a tissue sample, abiopsy, and/or any other physically extracted sample. At least abiological extraction may include an endocrinal sample. As a furthernon-limiting example, the at least a biological extraction may include asignal from at least a sensor configured to detect physiological data ofa user and recording the at least a biological extraction as a functionof the signal. At least a sensor 124 may include any medical sensorand/or medical device configured to capture sensor data concerning apatient, including any scanning, radiological and/or imaging device suchas without limitation x-ray equipment, computer assisted tomography(CAT) scan equipment, positron emission tomography (PET) scan equipment,any form of magnetic resonance imagery (MRI) equipment, ultrasoundequipment, optical scanning equipment such as photo-plethysmographicequipment, or the like. At least a sensor 124 may include anyelectromagnetic sensor, including without limitationelectroencephalographic sensors, magnetoencephalographic sensors,electrocardiographic sensors, electromyographic sensors, or the like. Atleast a sensor 124 may include a temperature sensor. At least a sensor124 may include any sensor that may be included in a mobile deviceand/or wearable device, including without limitation a motion sensorsuch as an inertial measurement unit (IMU), one or more accelerometers,one or more gyroscopes, one or more magnetometers, or the like. At leasta wearable and/or mobile device sensor may capture step, gait, and/orother mobility data, as well as data describing activity levels and/orphysical fitness. At least a wearable and/or mobile device sensor maydetect heart rate or the like. At least a sensor 124 may detect anyhematological parameter including blood oxygen level, pulse rate, heartrate, pulse rhythm, blood sugar, and/or blood pressure. At least asensor 108 may be configured to detect internal and/or externalbiomarkers and/or readings. At least a sensor 124 may be a part ofsystem 100 or may be a separate device in communication with system 100.

Still referring to FIG. 1, at least a biological extraction may includeany data suitable for use as physiological state data as describedabove, including without limitation any result of any medical test,physiological assessment, cognitive assessment, psychologicalassessment, or the like. System 100 may receive at least a biologicalextraction from one or more other devices after performance; system 100may alternatively or additionally perform one or more assessments and/ortests to obtain at least a biological extraction, and/or one or moreportions thereof, on system 100. For instance, at least biologicalextraction may include or more entries by a user in a form or similargraphical user interface object; one or more entries may include,without limitation, user responses to questions on a psychological,behavioral, personality, or cognitive test. For instance, at least aserver 102 may present to user a set of assessment questions designed orintended to evaluate a current state of mind of the user, a currentpsychological state of the user, a personality trait of the user, or thelike; at least a server 102 may provide user-entered responses to suchquestions directly as at least a biological extraction and/or mayperform one or more calculations or other algorithms to derive a scoreor other result of an assessment as specified by one or more testingprotocols, such as automated calculation of a Stanford-Binet and/orWechsler scale for IQ testing, a personality test scoring such as aMyers-Briggs test protocol, or other assessments that may occur topersons skilled in the art upon reviewing the entirety of thisdisclosure.

With continued reference to FIG. 1, at least a biological extraction mayinclude assessment and/or self-assessment data, and/or automated orother assessment results, obtained from a third-party device;third-party device may include, without limitation, a server or otherdevice (not shown) that performs automated cognitive, psychological,behavioral, personality, or other assessments. Third-party device mayinclude a device operated by an informed advisor.

Still referring to FIG. 1, at least a biological extraction may includedata describing one or more test results, including results of mobilitytests, stress tests, dexterity tests, endocrinal tests, genetic tests,and/or electromyographic tests, biopsies, radiological tests, genetictests, and/or sensory tests. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various additionalexamples of at least a physiological sample consistent with thisdisclosure. At least a physiological sample may be added to biologicalextraction database 200.

With continued reference to FIG. 1, system 100 may include a prognosticlabel learner 126 operating on the diagnostic engine 104, the prognosticlabel learner 126 designed and configured to generate the at least aprognostic output as a function of the first training set 106 and the atleast a biological extraction. Prognostic label learner 126 may includeany hardware and/or software module. Prognostic label learner 126 isdesigned and configured to generate outputs using machine learningprocesses. A machine learning process is a process that automatedly usesa body of data known as “training data” and/or a “training set” togenerate an algorithm that will be performed by a computingdevice/module to produce outputs given data provided as inputs; this isin contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 1, prognostic label learner 126 may be designedand configured to generate at least a prognostic output by creating atleast a first machine-learning model 128 relating physiological statedata 108 to prognostic labels using the first training set 106 andgenerating the at least a prognostic output using the firstmachine-learning model 128; at least a first machine-learning model 128may include one or more models that determine a mathematicalrelationship between physiological state data 108 and prognostic labels.Such models may include without limitation model developed using linearregression models. Linear regression models may include ordinary leastsquares regression, which aims to minimize the square of the differencebetween predicted outcomes and actual outcomes according to anappropriate norm for measuring such a difference (e.g. a vector-spacedistance norm); coefficients of the resulting linear equation may bemodified to improve minimization. Linear regression models may includeridge regression methods, where the function to be minimized includesthe least-squares function plus term multiplying the square of eachcoefficient by a scalar amount to penalize large coefficients. Linearregression models may include least absolute shrinkage and selectionoperator (LASSO) models, in which ridge regression is combined withmultiplying the least-squares term by a factor of 1 divided by doublethe number of samples. Linear regression models may include a multi-tasklasso model wherein the norm applied in the least-squares term of thelasso model is the Frobenius norm amounting to the square root of thesum of squares of all terms. Linear regression models may include theelastic net model, a multi-task elastic net model, a least angleregression model, a LARS lasso model, an orthogonal matching pursuitmodel, a Bayesian regression model, a logistic regression model, astochastic gradient descent model, a perceptron model, a passiveaggressive algorithm, a robustness regression model, a Huber regressionmodel, or any other suitable model that may occur to persons skilled inthe art upon reviewing the entirety of this disclosure. Linearregression models may be generalized in an embodiment to polynomialregression models, whereby a polynomial equation (e.g. a quadratic,cubic or higher-order equation) providing a best predicted output/actualoutput fit is sought; similar methods to those described above may beapplied to minimize error functions, as will be apparent to personsskilled in the art upon reviewing the entirety of this disclosure.

With continued reference to FIG. 1, machine-learning algorithms maygenerate prognostic output as a function of a classification of at leasta prognostic label. Classification as used herein includes pairing orgrouping prognostic labels as a function of a shared commonality.Classification may include for example, groupings, pairings, and/ortrends between physiological data and current prognostic label, futureprognostic label, and the like. In an embodiment, machine-learningalgorithms may examine relationships between a future propensity of auser to develop a condition based on current user physiological data.Machine-learning algorithms may include any and all algorithms asperformed by any modules, described herein for prognostic label learner126. For example, machine-learning algorithms may relate fasting bloodglucose readings of a user to user's future propensity to developdiabetes. Machine-learning algorithms may examine precursor conditionand future propensity to develop a subsequent disorder. For example,machine-learning algorithms may examine a user diagnosed with chickenpox and user's future propensity to subsequently develop shingles. Inyet another non-limiting example, machine-learning algorithms mayexamine infection with human papillomavirus (HPV) and subsequent cancerdiagnosis. Machine-learning algorithms may examine a user's propensityto have recurring attacks of a disease or condition, for example a userwith elevated uric acid levels and repeated attacks of gout.Machine-learning algorithms may examine user's genetic predisposition todevelop a certain condition or disease. For example, machine-learningalgorithms may examine presence of hereditary non-polyposis colorectalcancer (HNPCC) commonly known as lynch syndrome, and subsequentdiagnosis of colorectal cancer. In yet another non-limiting example,machine-learning algorithms may examine presence of abnormal squamouscells and/or abnormal glandular cells in the cervix and subsequentdevelopment of cervical cancer. Machine-learning algorithms may examineprogression of disease state, for example progression of humanimmunodeficiency virus (HIV) is marked by decline of CD4+ T-Cells, witha count below 200 leading to a diagnosis of acquired immunodeficiencysyndrome (AIDS). In yet another non-limiting example, progression ofdiabetes may be marked by increases of hemoglobin A1C levels with alevel of 6.5% indicating a diagnosis of diabetes. Machine-learningalgorithms may examine progression of disease by certain age groups. Forexample, progression of Multiple Sclerosis in users between the age of20-30 as compared to progression of Multiple Sclerosis in users betweenthe age of 70-80. Machine-learning algorithms may be examiningprogression of aging such as measurements of telomere length and/oroxidative stress levels and chance mortality risk. Machine-learningalgorithms may examine development of co-morbid conditions when adisease or conditions is already present. For example, machine-learningalgorithms may examine a user diagnosed with depression and subsequentdiagnosis of a co-morbid condition such as migraines, generalizedanxiety disorder, antisocial personality disorder, agoraphobia,obsessive-compulsive disorder, drug dependence alcohol dependence,and/or panic disorder. Machine-learning algorithms may examine a user'slifetime chance of developing a certain disease or condition, such as auser's lifetime risk of heart disease, Alzheimer's disease, diabetes andthe like. Machine-learning algorithms may be grouped and implementedaccording to any of the methodologies as described below in reference toFIG. 19.

Continuing to refer to FIG. 1, machine-learning algorithm used togenerate first machine-learning model 128 may include, withoutlimitation, linear discriminant analysis. Machine-learning algorithm mayinclude quadratic discriminate analysis. Machine-learning algorithms mayinclude kernel ridge regression. Machine-learning algorithms may includesupport vector machines, including without limitation support vectorclassification-based regression processes. Machine-learning algorithmsmay include stochastic gradient descent algorithms, includingclassification and regression algorithms based on stochastic gradientdescent. Machine-learning algorithms may include nearest neighbors'algorithms. Machine-learning algorithms may include Gaussian processessuch as Gaussian Process Regression. Machine-learning algorithms mayinclude cross-decomposition algorithms, including partial least squaresand/or canonical correlation analysis. Machine-learning algorithms mayinclude naïve Bayes methods. Machine-learning algorithms may includealgorithms based on decision trees, such as decision tree classificationor regression algorithms. Machine-learning algorithms may includeensemble methods such as bagging meta-estimator, forest of randomizedtress, AdaBoost, gradient tree boosting, and/or voting classifiermethods. Machine-learning algorithms may include neural net algorithms,including convolutional neural net processes.

Still referring to FIG. 1, prognostic label learner 126 may generateprognostic output using alternatively or additional artificialintelligence methods, including without limitation by creating anartificial neural network, such as a convolutional neural networkcomprising an input layer of nodes, one or more intermediate layers, andan output layer of nodes. Connections between nodes may be created viathe process of “training” the network, in which elements from a trainingdataset are applied to the input nodes, a suitable training algorithm(such as Levenberg-Marquardt, conjugate gradient, simulated annealing,or other algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning. This network may be trained using first trainingset 106; the trained network may then be used to apply detectedrelationships between elements of physiological state data 108 andprognostic labels.

With continued reference to FIG. 1, machine-learning algorithms mayinclude unsupervised processes; unsupervised processes may, as anon-limiting example, be executed by an unsupervised learning moduleexecuting on diagnostic engine 104 and/or on another computing device incommunication with diagnostic engine 104, which may include any hardwareor software module as described in more detail below in reference toFIG. 7. An unsupervised machine-learning process, as used herein, is aprocess that derives inferences in datasets without regard to labels; asa result, an unsupervised machine-learning process may be free todiscover any structure, relationship, and/or correlation provided in thedata. For instance, and without limitation, prognostic label learner 126and/or diagnostic engine 104 may perform an unsupervised machinelearning process on first training set 106, which may cluster data offirst training set 106 according to detected relationships betweenelements of the first training set 106, including without limitationcorrelations of elements of physiological state data 108 to each otherand correlations of prognostic labels to each other; such relations maythen be combined with supervised machine learning results to add newcriteria for prognostic label learner 126 to apply in relatingphysiological state data 108 to prognostic labels. As a non-limiting,illustrative example, an unsupervised process may determine that a firstelement of physiological data acquired in a blood test correlatesclosely with a second element of physiological data, where the firstelement has been linked via supervised learning processes to a givenprognostic label, but the second has not; for instance, the secondelement may not have been defined as an input for the supervisedlearning process, or may pertain to a domain outside of a domainlimitation for the supervised learning process. Continuing the example aclose correlation between first element of physiological state data 108and second element of physiological state data 108 may indicate that thesecond element is also a good predictor for the prognostic label; secondelement may be included in a new supervised process to derive arelationship or may be used as a synonym or proxy for the firstphysiological element by prognostic label learner 126.

Still referring to FIG. 1, diagnostic engine 104 and/or prognostic labellearner 126 may detect further significant categories of physiologicaldata, relationships of such categories to prognostic labels, and/orcategories of prognostic labels using machine-learning processes,including without limitation unsupervised machine-learning processes asdescribed above; such newly identified categories, as well as categoriesentered by experts in free-form fields as described above, may be addedto pre-populated lists of categories, lists used to identify languageelements for language learning module, and/or lists used to identifyand/or score categories detected in documents, as described above. In anembodiment, as additional data is added to system 100, prognostic labellearner 126 and/or diagnostic engine 104 may continuously or iterativelyperform unsupervised machine-learning processes to detect relationshipsbetween different elements of the added and/or overall data; in anembodiment, this may enable system 100 to use detected relationships todiscover new correlations between known biomarkers, prognostic labels,and/or ameliorative labels and one or more elements of data in largebodies of data, such as genomic, proteomic, and/or microbiome-relateddata, enabling future supervised learning and/or lazy learning processesas described in further detail below to identify relationships between,e.g., particular clusters of genetic alleles and particular prognosticlabels and/or suitable ameliorative labels. Use of unsupervised learningmay greatly enhance the accuracy and detail with which system may detectprognostic labels and/or ameliorative labels.

With continued reference to FIG. 1, unsupervised processes may besubjected to domain limitations. For instance, and without limitation,an unsupervised process may be performed regarding a comprehensive setof data regarding one person, such as a comprehensive medical history,set of test results, and/or physiological data such as genomic,proteomic, and/or other data concerning that persons. As anothernon-limiting example, an unsupervised process may be performed on dataconcerning a particular cohort of persons; cohort may include, withoutlimitation, a demographic group such as a group of people having ashared age range, ethnic background, nationality, sex, and/or gender.Cohort may include, without limitation, a group of people having ashared value for an element and/or category of physiological data, agroup of people having a shared value for an element and/or category ofprognostic label, and/or a group of people having a shared value and/orcategory of ameliorative label; as illustrative examples, cohort couldinclude all people having a certain level or range of levels of bloodtriglycerides, all people diagnosed with type II diabetes, all peoplewho regularly run between 10 and 15 miles per week, or the like. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of a multiplicity of ways in which cohorts and/or other sets ofdata may be defined and/or limited for a particular unsupervisedlearning process.

Still referring to FIG. 1, prognostic label learner 126 mayalternatively or additionally be designed and configured to generate atleast a prognostic output by executing a lazy learning process as afunction of the first training set 106 and the at least a biologicalextraction; lazy learning processes may be performed by a lazy learningmodule executing on diagnostic engine 104 and/or on another computingdevice in communication with diagnostic engine 104, which may includeany hardware or software module. A lazy-learning process and/orprotocol, which may alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover a “first guess” ata prognostic label associated with biological extraction, using firsttraining set 106. As a non-limiting example, an initial heuristic mayinclude a ranking of prognostic labels according to relation to a testtype of at least a biological extraction, one or more categories ofphysiological data identified in test type of at least a biologicalextraction, and/or one or more values detected in at least a biologicalextraction; ranking may include, without limitation, ranking accordingto significance scores of associations between elements of physiologicaldata and prognostic labels, for instance as calculated as describedabove. Heuristic may include selecting some number of highest-rankingassociations and/or prognostic labels. Prognostic label learner 126 mayalternatively or additionally implement any suitable “lazy learning”algorithm, including without limitation a K-nearest neighbors algorithm,a lazy naïve Bayes algorithm, or the like; persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variouslazy-learning algorithms that may be applied to generate prognosticoutputs as described in this disclosure, including without limitationlazy learning applications of machine-learning algorithms as describedin further detail below.

Continuing to refer to FIG. 1, prognostic label learner 126 may generatea plurality of prognostic labels having different implications for aparticular person. For instance, where the at least a physiologicalsample includes a result of a dexterity test, a low score may beconsistent with amyotrophic lateral sclerosis, Parkinson's disease,multiple sclerosis, and/or any number of less sever disorders ortendencies associated with lower levels of dexterity. In such asituation, prognostic label learner 126 and/or diagnostic engine 104 mayperform additional processes to resolve ambiguity. Processes may includepresenting multiple possible results to a medical practitioner,informing the medical practitioner that one or more follow-up testsand/or physiological samples are needed to further determine a moredefinite prognostic label. Alternatively or additionally, processes mayinclude additional machine learning steps; for instance, where referenceto a model generated using supervised learning on a limited domain hasproduced multiple mutually exclusive results and/or multiple resultsthat are unlikely all to be correct, or multiple different supervisedmachine learning models in different domains may have identifiedmutually exclusive results and/or multiple results that are unlikely allto be correct. In such a situation, prognostic label learner 126 and/ordiagnostic engine 104 may operate a further algorithm to determine whichof the multiple outputs is most likely to be correct; algorithm mayinclude use of an additional supervised and/or unsupervised model.Alternatively or additionally, prognostic label learner 126 may performone or more lazy learning processes using a more comprehensive set ofuser data to identify a more probably correct result of the multipleresults. Results may be presented and/or retained with rankings, forinstance to advise a medical professional of the relative probabilitiesof various prognostic labels being correct; alternatively oradditionally, prognostic labels associated with a probability ofcorrectness below a given threshold and/or prognostic labelscontradicting results of the additional process, may be eliminated. As anon-limiting example, an endocrinal test may determine that a givenperson has high levels of dopamine, indicating that a poor pegboardperformance is almost certainly not being caused by Parkinson's disease,which may lead to Parkinson's being eliminated from a list of prognosticlabels associated with poor pegboard performance, for that person.Similarly, a genetic test may eliminate Huntington's disease, or anotherdisease definitively linked to a given genetic profile, as a cause.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which additional processingmay be used to determine relative likelihoods of prognostic labels on alist of multiple prognostic labels, and/or to eliminate some labels fromsuch a list. Prognostic output 712 may be provided to user output deviceas described in further detail below.

Still referring to FIG. 1, diagnostic engine 104 includes anameliorative process label learner 130 operating on the diagnosticengine 104, the ameliorative process label learner 130 designed andconfigured to generate the at least an ameliorative output as a functionof the second training set 116 and the at least a prognostic output.Ameliorative process label learner 130 may include any hardware orsoftware module suitable for use as a prognostic label learner 126 asdescribed above. Ameliorative process label learner 130 is amachine-learning module as described above; ameliorative process labellearner 130 may perform any machine-learning process or combination ofprocesses suitable for use by a prognostic label learner 126 asdescribed above. For instance, and without limitation, and ameliorativeprocess label learner 130 may be configured to create a secondmachine-learning model 132 relating prognostic labels to ameliorativelabels using the second training set 116 and generate the at least anameliorative output using the second machine-learning model 132; secondmachine-learning model 132 may be generated according to any process,process steps, or combination of processes and/or process steps suitablefor creation of first machine learning model. In an embodiment,ameliorative process label learner 130 may use data from first trainingset 106 as well as data from second training set 116; for instance,ameliorative process label learner 130 may use lazy learning and/ormodel generation to determine relationships between elements ofphysiological data, in combination with or instead of prognostic labels,and ameliorative labels. Where ameliorative process label learner 130determines relationships between elements of physiological data andameliorative labels directly, this may determine relationships betweenprognostic labels and ameliorative labels as well owing to the existenceof relationships determined by prognostic label learner 126.

With continued reference to FIG. 1, system 100 includes a plan generatormodule 134 operating on the at least a server 102. Plan generator module134 may include any suitable hardware or hardware module. In anembodiment, plan generator module 134 is designed and configured togenerate a comprehensive instruction set associated with the user as afunction of the diagnostic output. In an embodiment, comprehensiveinstruction set 136 is a data structure containing instructions to beprovided to the user to explain the user's current prognostic status, asreflected by one or more prognostic outputs and provide the user with aplan based on the at least an ameliorative output, to achieve that. Inan embodiment, comprehensive instruction set 136 may be generated basedon at least an informed advisor output. Comprehensive instruction set136 may include but is not limited to a program, strategy, summary,recommendation, or any other type of interactive platform that may beconfigured to comprise information associated with the user, anapplicable verified external source, and one or more outputs derivedfrom the analyses performed on the extraction from the user.Comprehensive instruction set 136 may describe to a user a futureprognostic status to aspire to. Comprehensive instruction set 136 mayreflect analyses and diagnostics associated with a user. Plan generatormodule 134 may be configured to generate comprehensive instruction set136 including at least a nutrition instruction set and the supplementplan generator module may generate a supplement instruction set as afunction of the nutrition instruction set as described in more detailbelow. Nutrition instruction set is a data structure containinginstructions to the user including one or more recommendations as todietary and/or nutritional recommendations; such recommendations mayinclude, without limitation nutritional instructions, nutritionalcontent, digestibility, sample meal plans, foods and/or food groups toconsume or avoid, dietary recommendations that may reverse a deficiencyof a nutrient, ideal nutrition choices for a user and the like. In anembodiment, nutrition instruction set may be generated as a function ofbiological extraction and/or diagnostic output. Biological extractionmay include any of the biological extractions as described above. Forexample, a biological extraction of a user such as a blood sampleshowing low ferritin levels may be used to generate a nutritioninstruction set that may include recommendations to increase consumptionof iron rich foods such as lentils, spinach, tofu, broccoli, oysters,liver, and dried apricot. In such an instance, nutrition instruction setmay include suggested meals a user may cook and/or consume containingiron rich foods such as a recipe for a tofu stir-fry containing spinachand broccoli. In yet another non-limiting example, nutrition instructionset may be generated as a function of diagnostic output; for example adiagnostic output such as myocardial infarction may be utilized togenerate a nutrition instruction set that includes dietaryrecommendations that include plenty of vegetables, wholegrain breads,and lean meat.

With continued reference to FIG. 1, system 100 includes a supplementplan generator module 138 operating on the at least a server 102.Supplement plan generator module 138 may include any suitable hardwareor hardware module. Supplement plan generator module is designed andconfigured to generate a supplement instruction set 140 associated witha user as a function of the comprehensive instruction set 136. In anembodiment, supplement instruction set 140 is a data structurecontaining instructions to the user containing one or morerecommendations as to supplements that the user may want to considertaking. Supplements may include any products intended to supplement auser's diet. Supplements may include products consumed by a user thatcontain a dietary ingredient. Dietary ingredients may include anyvitamin, mineral, nutrient, homeopathic, amino acid, herb, botanical,nutraceutical, enzyme, health food, medical food, and the like.Supplements may contain dietary ingredients sourced from food,synthesized in a laboratory, and/or sourced in combination. Supplementsmay include for example, a multi-vitamin, co-enzyme q10, ubiquinol,resveratrol, probiotics such as Lactobacillus Acidophilus,Bifidobacterium Bifidum, Saccharomyces Boulardii, fish oil, B-Vitamincomplex, Vitamin D, cranberry, products containing combinationingredients, and the like. Supplements may be available in a variety ofdifferent dosage forms for a user to consume including for example,capsules, tablets, pills, buccal tablets, sub-lingual tablets,orally-disintegrating products, thin films, liquid solution, liquidsuspension, oil suspension, powder, solid crystals, seeds, foods,pastes, buccal films, inhaled forms such as aerosols, nebulizers, smokedforms, vaporized form, intradermal forms, subcutaneous forms,intramuscular forms, intraosseous forms, intraperitoneal forms,intravenous forms, creams, gels, balms, lotion, ointment, ear drops, eyedrops, skin patch, transdermal forms, vaginal rings, dermal patch,vaginal suppository, rectal suppository, urethral suppository, nasalsuppository, and the like. Supplements may be available to a userwithout a prescription such as for example, a fish oil supplement soldat a health food store. Supplements may be available to a user with aprescription, such as for example subcutaneous cyanocobalamin injectionsavailable at a compounding pharmacy. Supplements may be categorized intodifferent grade products such as for example pharmaceutical gradesupplements that may contain in excess of 99% purity and do not containbinders, fillers, excipients, dyes, or unknown substances and aremanufactured in Food and Drug Administration (FDA) registered facilitiesthat follow certified good manufacturing practices (cGMP); supplementsmay be of food grade quality such as for example supplements deemed tobe suitable for human consumption; supplements may be of feed gradequality such as for example supplements deemed to be suitable for animalconsumption. Supplement generator module 138 may transmit the supplementinstruction set associated with the user to a user client device.Transmission may include any of the transmission methodologies asdescribed herein, including network transmission. User client device mayinclude any of the user client devices as described in more detailbelow.

With continued reference to FIG. 1, supplement plan generator module 138may generate a supplement instruction set as a function of the nutritioninstruction set generated by the plan generator module. In anembodiment, nutrition instruction set generated by the plan generatormodule 134 may contain nutritional and/or dietary recommendations for auser including for example, sample meal plans and/or recipes that a usermay cook or consume over the course of a day, week, month and the like.Such information may then be utilized to generate supplement instructionset as a function of nutrition instruction set. For example, a nutritioninstruction set that includes recommendations for foods rich in calciumsuch as kale, almonds, oranges, and navy beans may be utilized togenerate a supplement instruction set that includes a calcium doseappropriate based on dietary intake of calcium. In such an instance,supplement instruction set may include a recommendation to not consumesupplemental calcium as enough may be consumed through diet alone. Inyet another non-limiting example, a nutrition instruction set thatrecommends a vegetarian and/or vegan diet may be utilized to generate asupplement instruction set that includes recommendations forsupplementation with iron and Vitamin B12 as enough cannot usually beobtained through diet alone for those following a vegetarian and/orvegan diet. In yet another non-limiting example, a nutrition instructionset that includes recommendation for a grain free diet may be utilizedto generate a supplement instruction set that includes recommendationsfor a b-complex as most users absorb dietary b-vitamins from grains andbreads.

In an embodiment, supplement instruction set may be generated as aresult of a user preference as to a nutrition instruction set. Forexample, a user may indicate an unwillingness to follow a nutritioninstruction set or a user may be unable to access ingredients and/orfoods contained within a nutrition instruction set. For example, asupplement instruction set containing a recommendation for a user toconsume broccoli extract may be generated because a user is unwilling toconsume cooked broccoli. In yet another non-limiting example, a user mayreceive a nutrition instruction set that includes recommendation toconsume magnesium rich cacao powder which a user may be unable tolocate, whereby supplement generator module may generate arecommendation to consume capsules instead.

With continued reference to FIG. 1, plan generator module 134 may beconfigured to generate a nutrition instruction as a function of thesupplement instruction set 140 generated by the supplement plangenerator module 138. Supplement instruction set 140 may informnutrition instruction set as to what foods should be avoided because ofsupplementation. In an embodiment, supplement instruction set 140 maycontain at least a recommended supplement such as a b-complex supplementfor a user with a diagnostic output such as neuropathy. In such aninstance, a supplement instruction set 140 containing a recommendationfor a b-complex may generate a nutrition instruction set that does notcontain a lot of foods containing b vitamins such as brown rice, barley,and millet. Supplement instruction set 140 may inform nutritioninstruction set as to what foods should be consumed in conjunction withsupplement instruction set 140 for example to assist in absorption ofsupplement or because supplementation may cause a deficiency in anothervitamin or mineral. For example, a supplement instruction set containingzinc supplementation may be used to generate a nutrition instruction setthat contains recommendations for consumption of copper containing foodssuch as oysters, nuts, seeds, shitake mushrooms, and lobster as zincsupplementation alone can cause a copper deficiency. Supplementinstruction set that includes ferrous sulfate may be used to generate anutrition instruction set that contains recommendations for consumptionof Vitamin C rich foods including broccoli, cantaloupe, cauliflower, redbell peppers, and kiwis in conjunction with ferrous sulfatesupplementation to increase absorption of ferrous sulfate. Supplementinstruction set 140 may inform nutrition instruction set as to timing ofwhen foods and meal contained within nutrition instruction set may beconsumed in conjunction and/or separately from supplements containedwith supplement instruction set 140. For example, supplement instructionset 140 containing selenium may need to be taken on an empty stomachwhile Vitamin D and Vitamin E may need to be taken with food to increaseabsorption.

With continued reference to FIG. 1, supplement plan generator module 138may be configured to receive a user preference to wean off a particularsupplement and instead obtain an ingredient from dietary sources. Insuch an instance, nutrition instruction set may be generated to aid auser in obtaining proper dietary sources of an ingredient or supplement.For example, a user who was supplementing with Vitamin D while living inMaine during the winter may seek to obtain adequate Vitamin D throughdiet and sun exposure upon moving to San Diego, whereby Vitamin Dsupplementation may be removed from supplement instruction set anddietary sources of Vitamin D such as eggs, liver, salmon, and milk maybe included in a nutrition instruction set. In yet another non-limitingexample, a user may be consuming magnesium supplements and may wish todiscontinue taking magnesium capsules whereby nutrition instruction setmay be generated to recommend foods and meals containing magnesium incomparable doses to what user was supplementing with such as tofu,almonds, dark chocolate and spinach.

With continued reference to FIG. 1, supplement plan generator module 138may be configured to receive at least an element of user data from auser client device. Element of user data as used herein, is any elementof data describing the user, user's needs, and/or user's preferences.Element of user data may include a dosage form preference. Dosage formpreference as used herein includes any preference a user may enterand/or select for a particular dosage form of a supplement. For example,a user may be unable to swallow large pills and may prefer to consume aparticular supplement as a liquid dosage form as opposed to a capsule ortablet. In yet another non-limiting example, a user may be disabled orelderly and may prefer a suppository dosage form instead of a dosageform taken by mouth. Element of user data may include dosage frequencypreference. Dosage frequency preference as used herein includes anypreference a user may enter and/or select as to how frequently a usermay prefer to consume a supplement. For example, a user may prefer toonly consume supplements that are dosed once per day because user isbusy and can't remember to take supplements at multiple times per day.In yet another non-limiting example, a user may prefer to takesupplements no more than three times per day with each meal, because anysupplement dosed more frequently than three times per day user willforget to take. Element of user data may include a quality preference.Quality preference as used herein includes any preference a user mayenter and/or select for a particular quality standard of a supplement.Quality standard may include quality standards as to ingredientscontained with a supplement, types of different fillers, binders, and/orpreservatives contained within a supplement, manufacturing processessuch as good manufacturing practices (GMP), United States Pharmacopeia(USP) certified supplements, NSF International certified supplements, ULcertified supplements, Consumerlab.com certified supplements, source ofingredients contained within a supplement such as those derived solelyfrom food sources, those derived solely from a laboratory, and/orcombination of the two, ingredient quality such as non-geneticallymodified organisms (GMO), organic ingredients, supplement grade such aspharmaceutical grade, food grade, feed grade, and the like. For example,a user may have a preference to avoid fillers such as magnesium stearateand maltodextrin. In yet another non-limiting example, a user may have apreference to consume supplements that only contain USP certification.

With continued reference to FIG. 1, supplement plan generator module 138may be configured to receive at least an element of advisory data froman advisor client device. Element of advisory data as used herein, isany element of data received from an informed advisor. Informed advisormay include, without limitation, a functional medical professional suchas a doctor, nurse, nurse practitioner, functional medicinepractitioner, any professional with a career in medicine, nutritiongenetics, fitness, life sciences, insurance, and/or any other applicableindustry that may contribute and advise a user as to a user's healthgoals and needs. An informed advisor may include a spiritual orphilosophical advisor, such as a religious leader, pastor, imam, rabbi,or the like. An informed advisor may include a physical fitness advisorsuch as without limitation a personal trainer, instructor in yoga ormartial arts, sports coach, or the like. An informed advisor may includea nutritional informed advisor such as a dietician, chef, andnutritionist who may offer expertise around a user's diet and nutritionstate and supplementation.

With continued reference to FIG. 1, supplement plan generator module 138may receive at least an element of advisory data from an advisor clientdevice. Advisory data may include a recommendation from an informedadvisor as to a supplement a user should be consuming. For example, aninformed advisor such as a fitness professional may recommend a user whois engaging in weightlifting exercises to supplement with creatine toassist in muscle building. In yet another non-limiting example, aninformed advisor such as a fitness professional may recommend a user whois engaging in marathon running to supplement with ubiquinol afterexercise to aid in muscle recovery. An informed advisor such as anutritionist may recommend a user following a Paleo diet to supplementwith fiber. Advisory data may include a contraindication such as asupplement that a user should not be consuming. For example, an informedadvisor such as a nutritionist may generate an advisory datum containinga contraindication to fish oil supplements for a user with a fishallergy. In yet another non-limiting example, an informed advisor suchas a personal trainer may generate an advisory datum containing acontraindication to Vitamin D supplementation for a user who is alreadyconsuming a multi-vitamin that contains Vitamin D. In an embodiment,contraindication may include input as to a supplement a user should notconsume. For example, an informed advisor such as a functional medicaldoctor may generate a contraindication for a user to not consume fishoil because the user is taking a blood thinning medication such aswarfarin. Advisory data may include a preference of an informed advisorfor a certain brand or standard of supplement. For example, an informedadvisor such as a functional nutritionist may generate an advisory datumfor a user that contains a preference for a specific brand of VitaminB12 that the functional nutritionist has used in the past. In yetanother non-limiting example, advisory datum may contain a preferencefrom an informed advisor such as a functional nutritionist for aspecific form of a supplement, such as a preference for methylcobalaminform of Vitamin B12 as opposed to hydroxocobalamin or cyanocobalamin.Advisory data may include a specific recommended dose of a supplementthat a user should supplement with. For example, an informed advisorsuch as a spiritual coach may recommend a user who is feeling stressedto supplement with 450 mg of valerian root once daily before bed.

With continued reference to FIG. 1, system 100 may include aclient-interface module 142. Client-interface module 142 may include anysuitable hardware or software module. Client-interface module 142 maydesigned and configured to transmit comprehensive instruction set 136 toat least a user client device 144 associated with the user. A userclient device 144 may include, without limitation, a display incommunication with diagnostic engine 104; display may include anydisplay as described herein. A user client device 144 may include anaddition computing device, such as a mobile device, laptop, desktopcomputer, or the like; as a non-limiting example, the user client device144 may be a computer and/or workstation operated by a medicalprofessional. Output may be displayed on at least a user client device144 using an output graphical user interface; output graphical userinterface may display at least a current prognostic descriptor, at leasta future prognostic descriptor, and/or at least an ameliorative processdescriptor.

With continued reference to FIG. 1, system 100 may include at least anadvisory module executing on the at least a server 102. At least anadvisory module 146 may include any suitable hardware or softwaremodule. In an embodiment, at least an advisory module 146 is designedand configured to generate at least an advisory output as a function ofthe comprehensive instruction set 136 and may transmit the advisoryoutput to at least an advisor client device 148. At least an advisorclient device 148 may include any device suitable for use as a userclient device 144 as described above. At least an advisor client device148 may operate on system 100 and may be a user client device 144 asdescribed above; that is, at least an advisory output may be output tothe user client device 144. Alternatively or additionally, at least anadvisor client device 148 may be operated by an informed advisor,defined for the purposes of this disclosure as any person besides theuser who has access to information useable to aid user in interactionwith artificial intelligence advisory system. An informed advisor mayinclude, without limitation, a medical professional such as a doctor,nurse, nurse practitioner, functional medicine practitioner, anyprofessional with a career in medicine, nutrition, genetics, fitness,life sciences, insurance, and/or any other applicable industry that maycontribute information and data to system 100 regarding medical needs.An informed advisor may include a spiritual or philosophical advisor,such as a religious leader, pastor, imam, rabbi, or the like. Aninformed advisor may include a physical fitness advisor, such as withoutlimitation a personal trainer, instructor in yoga or martial arts,sports coach, or the like. Advisory module 146 may generate at least anadvisory output while consulting information contained within advisorydatabase as described below in more detail.

Referring now to FIG. 2, data incorporated in first training set 106and/or second training set 116 may be incorporated in one or moredatabases. As a non-limiting example, one or elements of physiologicalstate data may be stored in and/or retrieved from a biologicalextraction database 200. A biological extraction database 200 mayinclude any data structure for ordered storage and retrieval of data,which may be implemented as a hardware or software module. A biologicalextraction database 200 may be implemented, without limitation, as arelational database, a key-value retrieval datastore such as a NOSQLdatabase, or any other format or structure for use as a datastore that aperson skilled in the art would recognize as suitable upon review of theentirety of this disclosure. A biological extraction database 200 mayinclude a plurality of data entries and/or records corresponding toelements of physiological data as described above. Data entries and/orrecords may describe, without limitation, data concerning particularphysiological samples that have been collected; entries may describereasons for collection of samples, such as without limitation one ormore conditions being tested for, which may be listed with relatedprognostic labels. Data entries may include prognostic labels and/orother descriptive entries describing results of evaluation of pastphysiological samples, including diagnoses that were associated withsuch samples, prognoses and/or conclusions regarding likelihood offuture diagnoses that were associated with such samples, and/or othermedical or diagnostic conclusions that were derived. Such conclusionsmay have been generated by system 100 in previous iterations of methods,with or without validation of correctness by medical professionals. Dataentries in a biological extraction database 200 may be flagged with orlinked to one or more additional elements of information, which may bereflected in data entry cells and/or in linked tables such as tablesrelated by one or more indices in a relational database; one or moreadditional elements of information may include data associating aphysiological sample and/or a person from whom a physiological samplewas extracted or received with one or more cohorts, includingdemographic groupings such as ethnicity, sex, age, income, geographicalregion, or the like, one or more common diagnoses or physiologicalattributes shared with other persons having physiological samplesreflected in other data entries, or the like. Additional elements ofinformation may include one or more categories of physiological data asdescribed above. Additional elements of information may includedescriptions of particular methods used to obtain physiological samples,such as without limitation physical extraction of blood samples or thelike, capture of data with one or more sensors, and/or any otherinformation concerning provenance and/or history of data acquisition.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which data entries in abiological extraction database 200 may reflect categories, cohorts,and/or populations of data consistently with this disclosure.

With continued reference to FIG. 2, diagnostic engine 104 may beconfigured to have a feedback mechanism. In an embodiment, diagnosticengine 104 may be configured to receive a first training set 200 and/ora second training set 220 generated by system 100. For example, dataabout a user that has been previously been analyzed by diagnostic engine104 may be utilized in algorithms by first model 240 and/or second model248. Such algorithms may be continuously updated as a function of suchdata. In yet another embodiment, data analyzed by language processingmodule 216 may be utilized as part of training data generatingalgorithms by first model 240 and/or second model 248 and/or any othermachine learning process performed by diagnostic engine 104.

Referring now to FIG. 3, one or more database tables in biologicalextraction database 200 may include, as a non-limiting example, aprognostic link table 300. Prognostic link table 300 may be a tablerelating physiological sample data as described above to prognosticlabels; for instance, where an expert has entered data relating aprognostic label to a category of physiological sample data and/or to anelement of physiological sample data via first graphical user interface112 as described above, one or more rows recording such an entry may beinserted in prognostic link table 300. Alternatively or additionally,linking of prognostic labels to physiological sample data may beperformed entirely in a prognostic label database as described below.

With continued reference to FIG. 3, biological extraction database 200may include tables listing one or more samples according to samplesource. For instance, and without limitation, biological extractiondatabase 200 may include a fluid sample table 304 listing samplesacquired from a person by extraction of fluids, such as withoutlimitation blood, lymph cerebrospinal fluid, or the like. As anothernon-limiting example, biological extraction database 200 may include asensor data table 308, which may list samples acquired using one or moresensors, for instance as described in further detail below. As a furthernon-limiting example, biological extraction database 200 may include agenetic sample table 312, which may list partial or entire sequences ofgenetic material. Genetic material may be extracted and amplified, as anon-limiting example, using polymerase chain reactions (PCR) or thelike. As a further example, also non-limiting, biological extractiondatabase 200 may include a medical report table 316, which may listtextual descriptions of medical tests, including without limitationradiological tests or tests of strength and/or dexterity or the like.Data in medical report table may be sorted and/or categorized using alanguage processing module 312, for instance, translating a textualdescription into a numerical value and a label corresponding to acategory of physiological data; this may be performed using any languageprocessing algorithm or algorithms as referred to in this disclosure. Asanother non-limiting example, biological extraction database 200 mayinclude a tissue sample table 320, which may record physiologicalsamples obtained using tissue samples. Tables presented above arepresented for exemplary purposes only; persons skilled in the art willbe aware of various ways in which data may be organized in biologicalextraction database 200 consistently with this disclosure.

Referring again to FIG. 2, diagnostic engine 104 and/or another devicein system 100 may populate one or more fields in biological extractiondatabase 200 using expert information, which may be extracted orretrieved from an expert knowledge database 204. An expert knowledgedatabase 204 may include any data structure and/or data store suitablefor use as a biological extraction database 200 as described above.Expert knowledge database 204 may include data entries reflecting one ormore expert submissions of data such as may have been submittedaccording to any process described above in reference to FIG. 1,including without limitation by using first graphical user interface 112and/or second graphical user interface 140. Expert knowledge databasemay include one or more fields generated by language processing module114, such as without limitation fields extracted from one or moredocuments as described above. For instance, and without limitation, oneor more categories of physiological data and/or related prognosticlabels and/or categories of prognostic labels associated with an elementof physiological state data as described above may be stored ingeneralized from in an expert knowledge database 204 and linked to,entered in, or associated with entries in a biological extractiondatabase 200. Documents may be stored and/or retrieved by diagnosticengine 104 and/or language processing module 114 in and/or from adocument database 208; document database 208 may include any datastructure and/or data store suitable for use as biological extractiondatabase 200 as described above. Documents in document database 208 maybe linked to and/or retrieved using document identifiers such as URIand/or URL data, citation data, or the like; persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variousways in which documents may be indexed and retrieved according tocitation, subject matter, author, date, or the like as consistent withthis disclosure.

Referring now to FIG. 4, an exemplary embodiment of an expert knowledgedatabase 204 is illustrated. Expert knowledge database 204 may, as anon-limiting example, organize data stored in the expert knowledgedatabase 204 according to one or more database tables. One or moredatabase tables may be linked to one another by, for instance, commoncolumn values. For instance, a common column between two tables ofexpert knowledge database 200 may include an identifier of an expertsubmission, such as a form entry, textual submission, expert paper, orthe like, for instance as defined below; as a result, a query may beable to retrieve all rows from any table pertaining to a givensubmission or set thereof. Other columns may include any other categoryusable for organization or subdivision of expert data, including typesof expert data, names and/or identifiers of experts submitting the data,times of submission, or the like; persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which expert data from one or more tables may be linked and/orrelated to expert data in one or more other tables.

Still referring to FIG. 4, one or more database tables in expertknowledge database 204 may include, as a non-limiting example, an expertprognostic table 400. Expert prognostic table 400 may be a tablerelating physiological sample data as described above to prognosticlabels; for instance, where an expert has entered data relating aprognostic label to a category of physiological sample data and/or to anelement of physiological sample data via first graphical user interface120 as described above, one or more rows recording such an entry may beinserted in expert prognostic table 400. In an embodiment, a formsprocessing module 404 may sort data entered in a submission via firstgraphical user interface 120 by, for instance, sorting data from entriesin the first graphical user interface 120 to related categories of data;for instance, data entered in an entry relating in the first graphicaluser interface 120 to a prognostic label may be sorted into variablesand/or data structures for storage of prognostic labels, while dataentered in an entry relating to a category of physiological data and/oran element thereof may be sorted into variables and/or data structuresfor the storage of, respectively, categories of physiological data orelements of physiological data. Where data is chosen by an expert frompre-selected entries such as drop-down lists, data may be storeddirectly; where data is entered in textual form, language processingmodule 114 may be used to map data to an appropriate existing label, forinstance using a vector similarity test or other synonym-sensitivelanguage processing test to map physiological data to an existing label.Alternatively or additionally, when a language processing algorithm,such as vector similarity comparison, indicates that an entry is not asynonym of an existing label, language processing module may indicatethat entry should be treated as relating to a new label; this may bedetermined by, e.g., comparison to a threshold number of cosinesimilarity and/or other geometric measures of vector similarity of theentered text to a nearest existent label, and determination that adegree of similarity falls below the threshold number and/or a degree ofdissimilarity falls above the threshold number. Data from expert textualsubmissions 408, such as accomplished by filling out a paper or PDF formand/or submitting narrative information, may likewise be processed usinglanguage processing module 114. Data may be extracted from expert papers412, which may include without limitation publications in medical and/orscientific journals, by language processing module 114 via any suitableprocess as described herein. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various additionalmethods whereby novel terms may be separated from already-classifiedterms and/or synonyms therefore, as consistent with this disclosure.Expert prognostic table 400 may include a single table and/or aplurality of tables; plurality of tables may include tables forparticular categories of prognostic labels such as a current diagnosistable, a future prognosis table, a genetic tendency table, a metabolictendency table, and/or an endocrinal tendency table (not shown), to namea few non-limiting examples presented for illustrative purposes only.

With continued reference to FIG. 4, one or more database tables inexpert knowledge database 204 may include, as a further non-limitingexample tables listing one or more ameliorative process labels; expertdata populating such tables may be provided, without limitation, usingany process described above, including entry of data from secondgraphical user interface 140 via forms processing module 404 and/orlanguage processing module 114, processing of textual submissions 408,or processing of expert papers 412. For instance, and withoutlimitation, an ameliorative nutrition table 416 may list one or moreameliorative processes based on nutritional instructions, and/or linksof such one or more ameliorative processes to prognostic labels, asprovided by experts according to any method of processing and/orentering expert data as described above. As a further example anameliorative action table 420 may list one or more ameliorativeprocesses based on instructions for actions a user should take,including without limitation exercise, meditation, and/or cessation ofharmful eating, substance abuse, or other habits, and/or links of suchone or more ameliorative processes to prognostic labels, as provided byexperts according to any method of processing and/or entering expertdata as described above. As an additional example, an ameliorativesupplement table 424 may list one or more ameliorative processes basedon nutritional supplements, such as vitamin pills or the like, and/orlinks of such one or more ameliorative processes to prognostic labels,as provided by experts according to any method of processing and/orentering expert data as described above. As a further non-limitingexample, an ameliorative medication table 428 may list one or moreameliorative processes based on medications, including withoutlimitation over-the-counter and prescription pharmaceutical drugs,and/or links of such one or more ameliorative processes to prognosticlabels, as provided by experts according to any method of processingand/or entering expert data as described above. As an additionalexample, a counterindication table 432 may list one or morecounter-indications for one or more ameliorative processes;counterindications may include, without limitation allergies to one ormore foods, medications, and/or supplements, side-effects of one or moremedications and/or supplements, interactions between medications, foods,and/or supplements, exercises that should not be used given one or moremedical conditions, injuries, disabilities, and/or demographiccategories, or the like. Tables presented above are presented forexemplary purposes only; persons skilled in the art will be aware ofvarious ways in which data may be organized in expert knowledge database204 consistently with this disclosure.

Referring again to FIG. 2, a prognostic label database 212, which may beimplemented in any manner suitable for implementation of biologicalextraction database 200, may be used to store prognostic labels used insystem 100, including any prognostic labels correlated with elements ofphysiological data in first training set 106 as described above;prognostic labels may be linked to or refer to entries in biologicalextraction database 200 to which prognostic labels correspond. Linkingmay be performed by reference to historical data concerningphysiological samples, such as diagnoses, prognoses, and/or othermedical conclusions derived from physiological samples in the past;alternatively or additionally, a relationship between a prognostic labeland a data entry in biological extraction database 200 may be determinedby reference to a record in an expert knowledge database 204 linking agiven prognostic label to a given category of physiological sample asdescribed above. Entries in prognostic label database 212 may beassociated with one or more categories of prognostic labels as describedabove, for instance using data stored in and/or extracted from an expertknowledge database 204.

Referring now to FIG. 5, an exemplary embodiment of a prognostic labeldatabase 212 is illustrated. Prognostic label database 212 may, as anon-limiting example, organize data stored in the prognostic labeldatabase 212 according to one or more database tables. One or moredatabase tables may be linked to one another by, for instance, commoncolumn values. For instance, a common column between two tables ofprognostic label database 212 may include an identifier of an expertsubmission, such as a form entry, textual submission, expert paper, orthe like, for instance as defined below; as a result, a query may beable to retrieve all rows from any table pertaining to a givensubmission or set thereof. Other columns may include any other categoryusable for organization or subdivision of expert data, including typesof expert data, names and/or identifiers of experts submitting the data,times of submission, or the like; persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which expert data from one or more tables may be linked and/orrelated to expert data in one or more other tables.

Still referring to FIG. 5, one or more database tables in prognosticlabel database 212 may include, as a non-limiting example, a sample datatable 500. Sample data table 500 may be a table listing sample data,along with, for instance, one or more linking columns to link such datato other information stored in prognostic label database 212. In anembodiment, sample data 504 may be acquired, for instance frombiological extraction database 200, in a raw or unsorted form, and maybe translated into standard forms, such as standard units ofmeasurement, labels associated with particular physiological datavalues, or the like; this may be accomplished using a datastandardization module 508, which may perform unit conversions. Datastandardization module 508 may alternatively or additionally map textualinformation, such as labels describing values tested for or the like,using language processing module 114 or equivalent components and/oralgorithms thereto.

Continuing to refer to FIG. 5, prognostic label database 212 may includea sample label table 512; sample label table 512 may list prognosticlabels received with and/or extracted from physiological samples, forinstance as received in the form of sample text 516. A languageprocessing module 114 may compare textual information so received toprognostic labels and/or form new prognostic labels according to anysuitable process as described above. Sample prognostic link table maycombine samples with prognostic labels, as acquired from sample labeltable and/or expert knowledge database 204; combination may be performedby listing together in rows or by relating indices or common columns oftwo or more tables to each other. Tables presented above are presentedfor exemplary purposes only; persons skilled in the art will be aware ofvarious ways in which data may be organized in expert knowledge database204 consistently with this disclosure.

Referring again to FIG. 2, first training set 106 may be populated byretrieval of one or more records from biological extraction database 200and/or prognostic label database 212; in an embodiment, entriesretrieved from biological extraction database 200 and/or prognosticlabel database 212 may be filtered and or select via query to match oneor more additional elements of information as described above, so as toretrieve a first training set 106 including data belonging to a givencohort, demographic population, or other set, so as to generate outputsas described below that are tailored to a person or persons with regardto whom system 100 classifies physiological samples to prognostic labelsas set forth in further detail below. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which records may be retrieved from biological extraction database200 and/or prognostic label database to generate a first training set toreflect individualized group data pertaining to a person of interest inoperation of system and/or method, including without limitation a personwith regard to whom at least a physiological sample is being evaluatedas described in further detail below. Diagnostic engine 104 mayalternatively or additionally receive a first training set 106 and storeone or more entries in biological extraction database 200 and/orprognostic label database 212 as extracted from elements of firsttraining set 106.

Still referring to FIG. 2, system 100 may include or communicate with anameliorative process label database 216; an ameliorative process labeldatabase 216 may include any data structure and/or datastore suitablefor use as a biological extraction database 200 as described above. Anameliorative process label database 216 may include one or more entrieslisting labels associated with one or more ameliorative processes asdescribed above, including any ameliorative labels correlated withprognostic labels in second training set 116 as described above;ameliorative process labels may be linked to or refer to entries inprognostic label database 212 to which ameliorative process labelscorrespond. Linking may be performed by reference to historical dataconcerning prognostic labels, such as therapies, treatments, and/orlifestyle or dietary choices chosen to alleviate conditions associatedwith prognostic labels in the past; alternatively or additionally, arelationship between an ameliorative process label and a data entry inprognostic label database 212 may be determined by reference to a recordin an expert knowledge database 204 linking a given ameliorative processlabel to a given category of prognostic label as described above.Entries in ameliorative process label database 212 may be associatedwith one or more categories of prognostic labels as described above, forinstance using data stored in and/or extracted from an expert knowledgedatabase 204.

Referring now to FIG. 6, an exemplary embodiment of an ameliorativeprocess label database 216 is illustrated. Ameliorative process labeldatabase 216 may, as a non-limiting example, organize data stored in theameliorative process label database 216 according to one or moredatabase tables. One or more database tables may be linked to oneanother by, for instance, common column values. For instance, a commoncolumn between two tables of ameliorative process label database 216 mayinclude an identifier of an expert submission, such as a form entry,textual submission, expert paper, or the like, for instance as definedbelow; as a result, a query may be able to retrieve all rows from anytable pertaining to a given submission or set thereof. Other columns mayinclude any other category usable for organization or subdivision ofexpert data, including types of expert data, names and/or identifiers ofexperts submitting the data, times of submission, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which expert data from one or more tablesmay be linked and/or related to expert data in one or more other tables.

Still referring to FIG. 6, ameliorative process label database 216 mayinclude a prognostic link table 600; prognostic link table may linkameliorative process data to prognostic label data, using any suitablemethod for linking data in two or more tables as described above.Ameliorative process label database 216 may include an ameliorativenutrition table 604, which may list one or more ameliorative processesbased on nutritional instructions, and/or links of such one or moreameliorative processes to prognostic labels, for instance as provided byexperts according to any method of processing and/or entering expertdata as described above, and/or using one or more machine-learningprocesses as set forth in further detail below. As a further example anameliorative action table 608 may list one or more ameliorativeprocesses based on instructions for actions a user should take,including without limitation exercise, meditation, and/or cessation ofharmful eating, substance abuse, or other habits, and/or links of suchone or more ameliorative processes to prognostic labels, as provided byexperts according to any method of processing and/or entering expertdata as described above and/or using one or more machine-learningprocesses as set forth in further detail below. As an additionalexample, an ameliorative supplement table 612 may list one or moreameliorative processes based on nutritional supplements, such as vitaminpills or the like, and/or links of such one or more ameliorativeprocesses to prognostic labels, as provided by experts according to anymethod of processing and/or entering expert data as described aboveand/or using one or more machine-learning processes as set forth infurther detail below. As a further non-limiting example, an ameliorativemedication table 616 may list one or more ameliorative processes basedon medications, including without limitation over-the-counter andprescription pharmaceutical drugs, and/or links of such one or moreameliorative processes to prognostic labels, as provided by expertsaccording to any method of processing and/or entering expert data asdescribed above and/or using one or more machine-learning processes asset forth in further detail below. As an additional example, acounterindication table 620 may list one or more counter-indications forone or more ameliorative processes; counterindications may include,without limitation allergies to one or more foods, medications, and/orsupplements, side-effects of one or more medications and/or supplements,interactions between medications, foods, and/or supplements, exercisesthat should not be used given one or more medical conditions, injuries,disabilities, and/or demographic categories, or the like; this may beacquired using expert submission as described above and/or using one ormore machine-learning processes as set forth in further detail below.Tables presented above are presented for exemplary purposes only;persons skilled in the art will be aware of various ways in which datamay be organized in ameliorative process database 216 consistently withthis disclosure

Referring again to FIG. 2, second training set 116 may be populated byretrieval of one or more records from prognostic label database 212and/or ameliorative process label database 216; in an embodiment,entries retrieved from prognostic label database 212 and/or ameliorativeprocess label database 216 may be filtered and or select via query tomatch one or more additional elements of information as described above,so as to retrieve a second training set 116 including data belonging toa given cohort, demographic population, or other set, so as to generateoutputs as described below that are tailored to a person or persons withregard to whom system 100 classifies prognostic labels to ameliorativeprocess labels as set forth in further detail below. Persons skilled inthe art, upon reviewing the entirety of this disclosure, will be awareof various ways in which records may be retrieved from prognostic labeldatabase 212 and/or ameliorative process label database 216 to generatea second training set 116 to reflect individualized group datapertaining to a person of interest in operation of system and/or method,including without limitation a person with regard to whom at least aphysiological sample is being evaluated as described in further detailbelow. Diagnostic engine 104 may alternatively or additionally receive asecond training set 116 and store one or more entries in prognosticlabel database 212 and/or ameliorative process label database 216 asextracted from elements of second training set 116.

With continued reference to FIG. 2, diagnostic engine 104 may receive anupdate to one or more elements of data represented in first training set106 and/or second training set 116, and may perform one or moremodifications to first training set 106 and/or second training set 116,or to biological extraction database 200, expert knowledge database 204,prognostic label database 212, and/or ameliorative process labeldatabase 216 as a result. For instance, a physiological sample may turnout to have been erroneously recorded; diagnostic engine 104 may removeit from first training set 106, second training set 116, biologicalextraction database 200, expert knowledge database 204, prognostic labeldatabase 212, and/or ameliorative process label database 216 as aresult. As a further example, a medical and/or academic paper, or astudy on which it was based, may be revoked; diagnostic engine 104 mayremove it from first training set 106, second training set 116,biological extraction database 200, expert knowledge database 204,prognostic label database 212, and/or ameliorative process labeldatabase 216 as a result. Information provided by an expert may likewisebe removed if the expert loses credentials or is revealed to have actedfraudulently.

Continuing to refer to FIG. 2, elements of data first training set 106,second training set 116, biological extraction database 200, expertknowledge database 204, prognostic label database 212, and/orameliorative process label database 216 may have temporal attributes,such as timestamps; diagnostic engine 104 may order such elementsaccording to recency, select only elements more recently entered forfirst training set 106 and/or second training set 116, or otherwise biastraining sets, database entries, and/or machine-learning models asdescribed in further detail below toward more recent or less recententries. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which temporal attributesof data entries may be used to affect results of methods and/or systemsas described herein.

Referring now to FIG. 7, machine-learning algorithms used by prognosticlabel learner 126 may include supervised machine-learning algorithms,which may, as a non-limiting example be executed using a supervisedlearning module 700 executing on diagnostic engine 104 and/or on anothercomputing device in communication with diagnostic engine 104, which mayinclude any hardware or software module. Supervised machine learningalgorithms, as defined herein, include algorithms that receive atraining set relating a number of inputs to a number of outputs, andseek to find one or more mathematical relations relating inputs tooutputs, where each of the one or more mathematical relations is optimalaccording to some criterion specified to the algorithm using somescoring function. For instance, a supervised learning algorithm may useelements of physiological data as inputs, prognostic labels as outputs,and a scoring function representing a desired form of relationship to bedetected between elements of physiological data and prognostic labels;scoring function may, for instance, seek to maximize the probabilitythat a given element of physiological state data 108 and/or combinationof elements of physiological data is associated with a given prognosticlabel and/or combination of prognostic labels to minimize theprobability that a given element of physiological state data 108 and/orcombination of elements of physiological state data 108 is notassociated with a given prognostic label and/or combination ofprognostic labels. Scoring function may be expressed as a risk functionrepresenting an “expected loss” of an algorithm relating inputs tooutputs, where loss is computed as an error function representing adegree to which a prediction generated by the relation is incorrect whencompared to a given input-output pair provided in first training set106. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various possible variations of supervisedmachine learning algorithms that may be used to determine relationbetween elements of physiological data and prognostic labels. In anembodiment, one or more supervised machine-learning algorithms may berestricted to a particular domain for instance, a supervisedmachine-learning process may be performed with respect to a given set ofparameters and/or categories of parameters that have been suspected tobe related to a given set of prognostic labels, and/or are specified aslinked to a medical specialty and/or field of medicine covering aparticular set of prognostic labels. As a non-limiting example, aparticular set of blood test biomarkers and/or sensor data may betypically used by cardiologists to diagnose or predict variouscardiovascular conditions, and a supervised machine-learning process maybe performed to relate those blood test biomarkers and/or sensor data tothe various cardiovascular conditions; in an embodiment, domainrestrictions of supervised machine-learning procedures may improveaccuracy of resulting models by ignoring artifacts in training data.Domain restrictions may be suggested by experts and/or deduced fromknown purposes for particular evaluations and/or known tests used toevaluate prognostic labels. Additional supervised learning processes maybe performed without domain restrictions to detect, for instance,previously unknown and/or unsuspected relationships betweenphysiological data and prognostic labels.

Referring now to FIG. 8, ameliorative process label learner 130 may beconfigured to perform one or more supervised learning processes, asdescribed above; supervised learning processes may be performed by asupervised learning module 800 executing on diagnostic engine 104 and/oron another computing device in communication with diagnostic engine 104,which may include any hardware or software module. For instance, asupervised learning algorithm may use prognostic labels as inputs,ameliorative labels as outputs, and a scoring function representing adesired form of relationship to be detected between prognostic labelsand ameliorative labels; scoring function may, for instance, seek tomaximize the probability that a given prognostic label and/orcombination of prognostic labels is associated with a given ameliorativelabel and/or combination of ameliorative labels to minimize theprobability that a given prognostic label and/or combination ofprognostic labels is not associated with a given ameliorative labeland/or combination of ameliorative labels. In an embodiment, one or moresupervised machine-learning algorithms may be restricted to a particulardomain; for instance, a supervised machine-learning process may beperformed with respect to a given set of parameters and/or categories ofprognostic labels that have been suspected to be related to a given setof ameliorative labels, for instance because the ameliorative processescorresponding to the set of ameliorative labels are hypothesized orsuspected to have an ameliorative effect on conditions represented bythe prognostic labels, and/or are specified as linked to a medicalspecialty and/or field of medicine covering a particular set ofprognostic labels and/or ameliorative labels. As a non-limiting example,a particular set prognostic labels corresponding to a set ofcardiovascular conditions may be typically treated by cardiologists, anda supervised machine-learning process may be performed to relate thoseprognostic labels to ameliorative labels associated with varioustreatment options, medications, and/or lifestyle changes.

With continued reference to FIG. 8, ameliorative process label learner130 may perform one or more unsupervised machine-learning processes asdescribed above; unsupervised processes may be performed by anunsupervised learning module 804 executing on diagnostic engine 104and/or on another computing device in communication with diagnosticengine 104, which may include any hardware or software module. Forinstance, and without limitation, ameliorative process label learner 130and/or diagnostic engine 104 may perform an unsupervised machinelearning process on second training set 116, which may cluster data ofsecond training set 116 according to detected relationships betweenelements of the second training set 116, including without limitationcorrelations of prognostic labels to each other and correlations ofameliorative labels to each other; such relations may then be combinedwith supervised machine learning results to add new criteria forameliorative process label learner 130 to apply in relating prognosticlabels to ameliorative labels. As a non-limiting, illustrative example,an unsupervised process may determine that a first prognostic label 110correlates closely with a second prognostic label 118, where the firstprognostic label 110 has been linked via supervised learning processesto a given ameliorative label, but the second has not; for instance, thesecond prognostic label 118 may not have been defined as an input forthe supervised learning process, or may pertain to a domain outside of adomain limitation for the supervised learning process. Continuing theexample, a close correlation between first prognostic label 110 andsecond prognostic label 118 may indicate that the second prognosticlabel 118 is also a good match for the ameliorative label; secondprognostic label 118 may be included in a new supervised process toderive a relationship or may be used as a synonym or proxy for the firstprognostic label 110 by ameliorative process label learner 130.Unsupervised processes performed by ameliorative process label learner130 may be subjected to any domain limitations suitable for unsupervisedprocesses performed by prognostic label learner 126 as described above.

Still referring to FIG. 8, diagnostic engine 104 and/or ameliorativeprocess label learner 130 may detect further significant categories ofprognostic labels, relationships of such categories to ameliorativelabels, and/or categories of ameliorative labels using machine-learningprocesses, including without limitation unsupervised machine-learningprocesses as described above; such newly identified categories, as wellas categories entered by experts in free-form fields as described above,may be added to pre-populated lists of categories, lists used toidentify language elements for language learning module, and/or listsused to identify and/or score categories detected in documents, asdescribed above. In an embodiment, as additional data is added to system100, ameliorative process label learner 130 and/or diagnostic engine 104may continuously or iteratively perform unsupervised machine-learningprocesses to detect relationships between different elements of theadded and/or overall data; in an embodiment, this may enable system 100to use detected relationships to discover new correlations between knownbiomarkers, prognostic labels, and/or ameliorative labels and one ormore elements of data in large bodies of data, such as genomic,proteomic, and/or microbiome-related data, enabling future supervisedlearning and/or lazy learning processes to identify relationshipsbetween, e.g., particular clusters of genetic alleles and particularprognostic labels and/or suitable ameliorative labels. Use ofunsupervised learning may greatly enhance the accuracy and detail withwhich system may detect prognostic labels and/or ameliorative labels.

With continued reference to FIG. 8, ameliorative labels may be generatedbased on classification of the at least a prognostic output.Classification as used herein includes pairing or grouping prognosticoutputs as a function of some shared commonality. Prognostic outputs maybe grouped with certain endocrine disorders such as diabetes, metabolicsyndrome, and/or pre-diabetes which may generate an ameliorative labelassociated with a physical exercise recommendation that may includeaerobic exercises such as running, brisk walking, cycling, and/orswimming in an attempt to reduce elevated blood sugar levels in patientswith such endocrine disorders. Prognostic outputs grouped with certainalarm conditions such as chest pains, shortness of breath, cold sweat,and sudden dizziness may generate an ameliorative label associated withmedical tests, diagnostics, and/or procedures for a suspected myocardialinfarction such as an electrocardiogram (EKG), measurement of serumtroponin levels, complete blood count (CBC), chest x-ray,echocardiogram, cardiac CT, cardiac MRI, and/or coronarycatheterization. Ameliorative label may be generated based on groupingssuch as severity of prognostic output. For example, a user who presentswith mild chest pain and some indigestion may be grouped to a categoryof prognostic labels that is serious but not alarming and may generatean ameliorative label that includes a blood test for troponin levels torule out a potential myocardial infarction. A user who presents withcrushing chest pain, tingling down one or both arms, shortness ofbreath, and cold and clammy skin may be grouped into a category of alarmso as to generate an ameliorative label that includes a cardiac CT orcardiac MRI to see if user is suffering from some type of coronaryocclusion and may be a candidate for a possible coronarycatheterization. In yet another non-limiting example, ameliorative labelmay be generated as a function of severity and/or progression ofprognostic output. For example, a prognostic label that includes adiagnosis of hypothyroidism as evidenced by a thyroid stimulating level(TSH) of 6.0 (normal range is 1.4-5.5) may generate an ameliorativelabel that includes 150 mcg per day of iodine supplementation to lowerTSH within normal limits due to mild TSH elevation and/or mildprogression of hypothyroidism. A prognostic output that includes adiagnosis of hypothyroidism as evidenced by a TSH of 15.0 may generatean ameliorative label that includes 300 mcg per day of iodinesupplementation as well as a prescription for a T-4 containingmedication such as Synthroid and a T-3 containing medication such asCytomel due to the more severe progression of hypothyroidism.Classification of at least a prognostic output may include staging of aprognostic label. Staging may include dividing a disease state orcondition into categories on a spectrum of disease progression andsymptomology. For example, a user with a prognostic output thatindicates peri-menopause as evidenced by increasing prevalence of hotflashes may generate an ameliorative label that includes arecommendation for supplementation with black cohosh, while a user witha prognostic output that indicates progression to menopause as evidencedby persistent hot flashes, night sweats, absence of menstruation, dryhair, and fatigue may generate an ameliorative label that containsrecommendations for supplementation with bio-identical hormonereplacement therapy such as estrone (E1), estradiol (E2), estriol (E3),progesterone, testosterone, dehydroepiandrosterone (DHEA), and/orpregnenolone. In yet another non-limiting example, early stage of adisease such as Alzheimer's disease as demonstrated by mild cognitiveimpairment may generate an ameliorative label that includes norecommended medical treatment except for watchful waiting. However,advanced Alzheimer's disease may warrant an ameliorative label thatincludes medical intervention and may require a prescription medication.Ameliorative label may be generated by any of the methodologies asdescribed herein.

Continuing to view FIG. 8, ameliorative process label learner 130 may beconfigured to perform a lazy learning process as a function of thesecond training set 116 and the at least a prognostic output to producethe at least an ameliorative output; a lazy learning process may includeany lazy learning process as described above regarding prognostic labellearner 126. Lazy learning processes may be performed by a lazy learningmodule 808 executing on diagnostic engine 104 and/or on anothercomputing device in communication with diagnostic engine 104, which mayinclude any hardware or software module. Ameliorative output 812 may beprovided to a user output device as described in further detail below.

In an embodiment, and still referring to FIG. 8, ameliorative processlabel learner 130 may generate a plurality of ameliorative labels havingdifferent implications for a particular person. For instance, where aprognostic label indicates that a person has a magnesium deficiency,various dietary choices may be generated as ameliorative labelsassociated with correcting the deficiency, such as ameliorative labelsassociated with consumption of almonds, spinach, and/or dark chocolate,as well as ameliorative labels associated with consumption of magnesiumsupplements. In such a situation, ameliorative process label learner 130and/or diagnostic engine 104 may perform additional processes to resolveambiguity. Processes may include presenting multiple possible results toa medical practitioner, informing the medical practitioner of variousoptions that may be available, and/or that follow-up tests, procedures,or counseling may be required to select an appropriate choice.Alternatively or additionally, processes may include additional machinelearning steps. For instance, ameliorative process label learner 130 mayperform one or more lazy learning processes using a more comprehensiveset of user data to identify a more probably correct result of themultiple results. Results may be presented and/or retained withrankings, for instance to advise a medical professional of the relativeprobabilities of various ameliorative labels being correct or idealchoices for a given person; alternatively or additionally, ameliorativelabels associated with a probability of success or suitability below agiven threshold and/or ameliorative labels contradicting results of theadditional process, may be eliminated. As a non-limiting example, anadditional process may reveal that a person is allergic to tree nuts,and consumption of almonds may be eliminated as an ameliorative label tobe presented.

Continuing to refer to FIG. 8, ameliorative process label learner 130may be designed and configured to generate further training data and/orto generate outputs using longitudinal data 816. As used herein,longitudinal data 816 may include a temporally ordered series of dataconcerning the same person, or the same cohort of persons; for instance,longitudinal data 816 may describe a series of blood samples taken oneday or one month apart over the course of a year. Longitudinal data 816may related to a series of samples tracking response of one or moreelements of physiological data recorded regarding a person undergoingone or more ameliorative processes linked to one or more ameliorativeprocess labels. Ameliorative process label learner 130 may track one ormore elements of physiological data and fit, for instance, a linear,polynomial, and/or splined function to data points; linear, polynomial,or other regression across larger sets of longitudinal data, using, forinstance, any regression process as described above, may be used todetermine a best-fit graph or function for the effect of a givenameliorative process over time on a physiological parameter. Functionsmay be compared to each other to rank ameliorative processes; forinstance, an ameliorative process associated with a steeper slope incurve representing improvement in a physiological data element, and/or ashallower slope in a curve representing a slower decline, may be rankedhigher than an ameliorative process associated with a less steep slopefor an improvement curve or a steeper slope for a curve marking adecline. Ameliorative processes associated with a curve and/or terminaldata point representing a value that does not associate with apreviously detected prognostic label may be ranked higher than one thatis not so associated. Information obtained by analysis of longitudinaldata 816 may be added to ameliorative process database and/or secondtraining set.

Referring now to FIG. 9, an exemplary embodiment of a plan generatormodule 134 is illustrated. Comprehensive instruction set 136 includes atleast a current prognostic descriptor 900 which as used in thisdisclosure is an element of data describing a current prognostic statusbased on at least one prognostic output. Plan generator module 134 mayproduce at least a current prognostic descriptor 900 using at least aprognostic output. In an embodiment, plan generator module 134 mayinclude a label synthesizer 904. Label synthesizer 904 may include anysuitable software or hardware module. In an embodiment, labelsynthesizer 904 may be designed and configured to combine a plurality oflabels in at least a prognostic output together to provide maximallyefficient data presentation. Combination of labels together may includeelimination of duplicate information. For instance, label synthesizer904 and/or at least a server 102 may be designed and configure todetermine a first prognostic label of the at least a prognostic label isa duplicate of a second prognostic label of the at least a prognosticlabel and eliminate the first prognostic label. Determination that afirst prognostic label is a duplicate of a second prognostic label mayinclude determining that the first prognostic label is identical to thesecond prognostic label; for instance, a prognostic label generated fromtest data presented in one biological extraction of at least abiological extraction may be the same as a prognostic label generatedfrom test data presented in a second biological extraction of at least abiological extraction. As a further non-limiting example, a firstprognostic label may be synonymous with a second prognostic label, wheredetection of synonymous labels may be performed, without limitation, bya language processing module 114 as described above.

Continuing to refer to FIG. 10, label synthesizer 904 may groupprognostic labels according to one or more classification systemsrelating the prognostic labels to each other. For instance, plangenerator module 134 and/or label synthesizer 904 may be configured todetermine that a first prognostic label of the at least a prognosticlabel and a second prognostic label of the at least a prognostic labelbelong to a shared category. A shared category may be a category ofconditions or tendencies toward a future condition to which each offirst prognostic label and second prognostic label belongs; as anexample, lactose intolerance and gluten sensitivity may each be examplesof digestive sensitivity, for instance, which may in turn share acategory with food sensitivities, food allergies, digestive disorderssuch as celiac disease and diverticulitis, or the like. Shared categoryand/or categories may be associated with prognostic labels as well. Agiven prognostic label may belong to a plurality of overlappingcategories. Plan generator module 134 may be configured to add acategory label associated with a shared category to comprehensiveinstruction set 136, where addition of the label may include addition ofthe label and/or a datum linked to the label, such as a textual ornarrative description. In an embodiment, relationships betweenprognostic labels and categories may be retrieved from a prognosticlabel classification database 908, for instance by generating a queryusing one or more prognostic labels of at least a prognostic output,entering the query, and receiving one or more categories matching thequery from the prognostic label classification database 908.

Referring now to FIG. 10, an exemplary embodiment of a prognostic labelclassification database 908 is illustrated. Prognostic labelclassification database 908 may be implemented as any database and/ordatastore suitable for use as biological extraction database 200 asdescribed above. One or more database tables in prognostic labelclassification database 908 may include, without limitation, asymptomatic classification table 1000; symptomatic classification table1000 may relate each prognostic label to one or more categories ofsymptoms associated with that prognostic label. As a non-limitingexample, symptomatic classification table 1000 may include recordsindicating that each of lactose intolerance and gluten sensitivityresults in symptoms including gas buildup, bloating, and abdominal pain.One or more database tables in prognostic label classification database1108 may include, without limitation, a systemic classification table1004; systemic classification table 1004 may relate each prognosticlabel to one or more systems associated with that prognostic label. As anon-limiting example, systemic classification table 1004 may includerecords indicating each of lactose intolerance and gluten sensitivityaffects the digestive system; two digestive sensitivities linked toallergic or other immune responses may additionally be linked insystemic classification table 1004 to the immune system. One or moredatabase tables in prognostic label classification database 908 mayinclude, without limitation, a body part classification table 1008; bodypart classification table 1008 may relate each prognostic label to oneor more body parts associated with that prognostic label. As anon-limiting example, body part classification table 1008 may includerecords indicating each of psoriasis and rosacea affects the skin of aperson. One or more database tables in prognostic label classificationdatabase 908 may include, without limitation, a causal classificationtable 1012; causal classification table 1012 may relate each prognosticlabel to one or more causes associated with that prognostic label. As anon-limiting example, causal classification table 1012 may includerecords indicating each of type 2 diabetes and hypertension may haveobesity as a cause. The above-described tables, and entries therein, areprovided solely for exemplary purposes. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousadditional examples for tables and/or relationships that may be includedor recorded in prognostic classification table consistently with thisdisclosure.

Referring again to FIG. 9, plan generator module 134 may be configuredto generate current prognostic descriptor 900 by converting one or moreprognostic labels into narrative language. As a non-limiting example,plan generator module 134 may include a narrative language unit 912,which may be configured to determine an element of narrative languageassociated with at least a prognostic label and include the element ofnarrative language in current prognostic label descriptor. Narrativelanguage unit 912 may implement this, without limitation, by using alanguage processing module 114 to detect one or more associationsbetween prognostic labels, or lists of prognostic labels, and phrasesand/or statements of narrative language. Alternatively or additionally,narrative language unit 912 may retrieve one or more elements ofnarrative language from a narrative language database 916, which maycontain one or more tables associating prognostic labels and/or groupsof prognostic labels with words, sentences, and/or phrases of narrativelanguage. One or more elements of narrative language may be included incomprehensive instruction set 136, for instance for display to a user astext describing a current prognostic status of the user. Currentprognostic descriptor 900 may further include one or more images; one ormore images may be retrieved by plan generator module 134 from an imagedatabase 920, which may contain one or more tables associatingprognostic labels, groups of prognostic labels, current prognosticdescriptors 1000, or the like with one or more images.

With continued reference to FIG. 9, comprehensive instruction set 136may include one or more follow-up suggestions, which may include,without limitation, suggestions for acquisition of an additionalbiological extraction; in an embodiment, additional biologicalextraction may be provided to diagnostic engine 104, which may triggerrepetition of one or more processes as described above, includingwithout limitation generation of prognostic output, refinement orelimination of ambiguous prognostic labels of prognostic output,generation of ameliorative output, and/or refinement or elimination ofambiguous ameliorative labels of ameliorative output. For instance,where a pegboard test result suggests possible diagnoses of Parkinson'sdisease, Huntington's disease, ALS, and MS as described above, follow-upsuggestions may include suggestions to perform endocrinal tests, genetictests, and/or electromyographic tests; results of such tests mayeliminate one or more of the possible diagnoses, such that asubsequently displayed output only lists conditions that have not beeneliminated by the follow-up test. Follow-up tests may include anyreceipt of any biological extraction as described above.

With continued reference to FIG. 9, comprehensive instruction set 136may include one or more elements of contextual information, includingwithout limitation any patient medical history such as current labresults, a current reason for visiting a medical professional, currentstatus of one or more currently implemented treatment plans,biographical information concerning the patient, and the like. One ormore elements of contextual information may include goals a patientwishes to achieve with a medical visit or session, and/or as result ofinteraction with diagnostic engine 104. Contextual information mayinclude one or more questions a patient wishes to have answered in amedical visit and/or session, and/or as a result of interaction withdiagnostic engine 104. Contextual information may include one or morequestions to ask a patient. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various forms ofcontextual information that may be included, consistently with thisdisclosure.

With continued reference to FIG. 9, comprehensive instruction set 136may include at least a future prognostic descriptor 924. As used herein,a future prognostic descriptor 924 is an element of data describing afuture prognostic status based on at least one prognostic output, whichmay include without limitation a desired further prognostic status. Inan embodiment, future prognostic descriptor 924 may include any elementsuitable for inclusion in current prognostic descriptor 900. Futureprognostic descriptor 924 may be generated using any processes, modules,and/or components suitable for generation of current prognosticdescriptor 900 as described above.

Still referring to FIG. 9, comprehensive instruction set 136 includes atleast an ameliorative process descriptor 928, which as defined in thisdisclosure an element of data describing one or more ameliorativeprocesses to be followed based on at least one ameliorative output; atleast an ameliorative process descriptor 928 may include descriptors forameliorative processes usable to achieve future prognostic descriptor924. Plan generator module 134 may produce at least an ameliorativeprocess descriptor 928 using at least a prognostic output. In anembodiment, label synthesizer 904 may be designed and configured tocombine a plurality of labels in at least an ameliorative outputtogether to provide maximally efficient data presentation. Combinationof labels together may include elimination of duplicate information. Forinstance, label synthesizer 904 and/or at least a server 102 may bedesigned and configure to determine a first ameliorative label of the atleast an ameliorative label is a duplicate of a second ameliorativelabel of the at least an ameliorative label and eliminate the firstameliorative label. Determination that a first ameliorative label is aduplicate of a second ameliorative label may include determining thatthe first ameliorative label is identical to the second ameliorativelabel; for instance, a ameliorative label generated from test datapresented in one biological extraction of at least a biologicalextraction may be the same as a ameliorative label generated from testdata presented in a second biological extraction of at least abiological extraction. As a further non-limiting example, a firstameliorative label may be synonymous with a second ameliorative label,where detection of synonymous labels may be performed, withoutlimitation, by a language processing module 114 as described above.

Continuing to refer to FIG. 9, label synthesizer 904 may groupameliorative labels according to one or more classification systemsrelating the ameliorative labels to each other. For instance, plangenerator module 134 and/or label synthesizer 904 may be configured todetermine that a first ameliorative label of the at least anameliorative label and a second ameliorative label of the at least anameliorative label belong to a shared category. A shared category may bea category of conditions or tendencies toward a future condition towhich each of first ameliorative label and second ameliorative labelbelongs; as an example, lactose intolerance and gluten sensitivity mayeach be examples of digestive sensitivity, for instance, which may inturn share a category with food sensitivities, food allergies, digestivedisorders such as celiac disease and diverticulitis, or the like. Sharedcategory and/or categories may be associated with ameliorative labels aswell. A given ameliorative label may belong to a plurality ofoverlapping categories. Plan generator module 134 may be configured toadd a category label associated with a shared category to comprehensiveinstruction set 136, where addition of the label may include addition ofthe label and/or a datum linked to the label, such as a textual ornarrative description. In an embodiment, relationships betweenameliorative labels and categories may be retrieved from an ameliorativelabel classification database 932, for instance by generating a queryusing one or more ameliorative labels of at least an ameliorativeoutput, entering the query, and receiving one or more categoriesmatching the query from the ameliorative label classification database932.

Referring now to FIG. 11, an exemplary embodiment of an ameliorativelabel classification database 932 is illustrated. Ameliorative labelclassification database 932 may be implemented as any database and/ordatastore suitable for use as biological extraction database 200 asdescribed above. One or more database tables in ameliorative labelclassification database 932 may include, without limitation, anintervention category table 1100; intervention 1200 may relate eachameliorative label to one or more categories associated with thatameliorative label. As a non-limiting example, intervention categorytable 1100 may include records indicating that each of a plan to consumea given quantity of almonds and a plan to consume less meat maps to acategory of nutritional instruction, while a plan to jog for 30 minutesper day maps to a category of activity. One or more database tables inameliorative label classification database 932 may include, withoutlimitation, a nutrition category table 1104; nutrition category table1104 may relate each ameliorative label pertaining to nutrition to oneor more categories associated with that ameliorative label. As anon-limiting example, nutrition category table 1104 may include recordsindicating that each of a plan to consume more almonds and a plan toconsume more walnuts qualifies as a plan to consume more nuts, as wellas a plan to consume more protein. One or more database tables inameliorative label classification database 932 may include, withoutlimitation, an action category table 1108; action category table 1108may relate each ameliorative label pertaining to an action to one ormore categories associated with that ameliorative label. As anon-limiting example, action category table 1108 may include recordsindicating that each of a plan jog for 30 minutes a day and a plan toperform a certain number of sit-ups per day qualifies as an exerciseplan. One or more database tables in ameliorative label classificationdatabase 932 may include, without limitation, a medication categorytable 1112; medication category table 1112 may relate each ameliorativelabel associated with a medication to one or more categories associatedwith that ameliorative label. As a non-limiting example, medicationcategory table 1112 may include records indicating that each of a planto take an antihistamine and a plan to take an anti-inflammatory steroidbelongs to a category of allergy medications. One or more databasetables in ameliorative label classification database 932 may include,without limitation, a supplement category table 1116; supplementcategory table 1116 may relate each ameliorative label pertaining to asupplement to one or more categories associated with that ameliorativelabel. As a non-limiting example, supplement category table 1116 mayinclude records indicating that each of a plan to consume a calciumsupplement and a plan to consume a vitamin D supplement corresponds to acategory of supplements to aid in bone density. Ameliorative labels maybe mapped to each of nutrition category table 1104, action categorytable 1108, supplement category table 1116, and medication categorytable 1112 using intervention category table 1100. The above-describedtables, and entries therein, are provided solely for exemplary purposes.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various additional examples for tablesand/or relationships that may be included or recorded in ameliorativeclassification table consistently with this disclosure.

Referring again to FIG. 9, plan generator module 134 may be configuredto generate ameliorative process descriptor 928 by converting one ormore ameliorative labels into narrative language. As a non-limitingexample, plan generator module 134 may include a narrative language unit912, which may be configured to determine an element of narrativelanguage associated with at least an ameliorative label and include theelement of narrative language in current ameliorative label descriptor.Narrative language unit 912 may implement this, without limitation, byusing a language processing module 114 to detect one or moreassociations between ameliorative labels, or lists of ameliorativelabels, and phrases and/or statements of narrative language.Alternatively or additionally, narrative language unit 912 may retrieveone or more elements of narrative language from narrative languagedatabase 916, which may contain one or more tables associatingameliorative labels and/or groups of ameliorative labels with words,sentences, and/or phrases of narrative language. One or more elements ofnarrative language may be included in comprehensive instruction set 136,for instance for display to a user as text describing a currentameliorative status of the user. Ameliorative process descriptor 928 mayfurther include one or more images; one or more images may be retrievedby plan generator module 134 from an image database 920, which maycontain one or more tables associating ameliorative labels, groups ofameliorative labels, ameliorative process descriptors 928, or the likewith one or more images.

With continued reference to FIG. 9, plan generator module 134 mayinclude nutrition instruction set 940. In an embodiment, plan generatormodule 134 may generate comprehensive instruction set 136 including anutrition instruction set 940. Nutrition instruction set is a datastructure containing instructions to the user including one or morerecommendations as to dietary and/or nutritional recommendationsincluding nutritional instructions, nutritional content, digestibility,sample meal plans, foods and/or food groups to consume or avoid, dietaryrecommendations that may reverse a deficiency of a nutrient, idealnutrition choices for a user and the like. In an embodiment, nutritioninstruction set may be generated as a function of biological extractionand/or diagnostic output as described above in more detail in referenceto FIG. 1.

Referring now to FIG. 12, and exemplary embodiment of a narrativelanguage database 916 is illustrated. Narrative language database 916may be implemented as any database and/or datastore suitable for use asbiological extraction database 200 as described above. One or moredatabase tables in narrative language database 916 may include, withoutlimitation, a prognostic description table 1200, which may linkprognostic labels to narrative descriptions associated with prognosticlabels. One or more database tables in narrative language database 916may include, without limitation, an ameliorative description table 1204,which may link ameliorative process labels to narrative descriptionsassociated with ameliorative process labels. One or more database tablesin narrative language database 916 may include, without limitation, acombined description table 1208, which may link combinations ofprognostic labels and ameliorative labels to narrative descriptionsassociated with the combinations. One or more database tables innarrative language database 916 may include, without limitation, aparagraph template table 1212, which may contain one or more templatesof paragraphs, pages, reports, or the like into which images and text,such as images obtained from image database 920 and text obtained fromprognostic description table 1200, ameliorative description table 1204,and combined description table 1208 may be inserted. Tables in narrativelanguage database 916 may be populated, as a non-limiting example, usingsubmissions from experts, which may be collected according to anyprocesses described above. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various way sin whichentries in narrative language database 916 may be categorized and/ororganized.

Referring now to FIG. 13, an exemplary embodiment of an image database920 is illustrated. Image database 920 may be implemented as anydatabase and/or datastore suitable for use as biological extractiondatabase 200 as described above. One or more database tables in imagedatabase 920 may include, without limitation, a prognostic image table1300, which may link prognostic labels to images associated withprognostic labels. One or more database tables in image database 920 mayinclude, without limitation, an ameliorative image table 1304, which maylink ameliorative process labels to images associated with ameliorativeprocess labels. One or more database tables in image database 920 mayinclude, without limitation, a combined description table 1308, whichmay link combinations of prognostic labels and ameliorative labels toimages associated with the combinations. One or more database tables inimage database 920 may include, without limitation, a prognostic videotable 1312, which may link prognostic labels to videos associated withprognostic labels. One or more database tables in image database 920 mayinclude, without limitation, an ameliorative video table 1316, which maylink ameliorative process labels to videos associated with ameliorativeprocess labels. One or more database tables in image database 920 mayinclude, without limitation, a combined video table 1320, which may linkcombinations of prognostic labels and ameliorative labels to videosassociated with the combinations. Tables in image database 920 may bepopulated, without limitation, by submissions by experts, which may beprovided according to any process or process steps described in thisdisclosure for collection of expert submissions.

Referring again to FIG. 10, plan generator module 134 may be configuredto receive at least an element of user data and filter diagnostic outputusing the at least an element of user data. At least an element of userdata, as used herein, is any element of data describing the user, userneeds, and/or user preferences. At least an element of user data mayinclude a constitutional restriction. At least a constitutionalrestriction may include any health-based reason that a user may beunable to engage in a given ameliorative process; at least aconstitutional restriction may include any counter-indication asdescribed above, including an injury, a diagnosis of somethingpreventing use of one or more ameliorative processes, an allergy orfood-sensitivity issue, a medication that is counter-indicated, or thelike. At least an element of user data may include at least a userpreference. At least a user preference may include, without limitation,any preference to engage in or eschew any ameliorative process and/orother potential elements of a comprehensive instruction set 136,including religious preferences such as forbidden foods, medicalinterventions, exercise routines, or the like.

Referring now to FIG. 14, an exemplary embodiment of a user database 936is illustrated. User database 936 may be implemented as any databaseand/or datastore suitable for use as described above. One or moredatabase tables in user database 936 may include, without limitation, aconstitution restriction table 1400; at least a constitutionalrestriction may be linked to a given user and/or user identifier in aconstitutional restriction table 1400. One or more database tables inuser database 936 may include, without limitation, a user preferencetable 1404; at least a user preference may be linked to a given userand/or user identifier in a user preference table 1404.

Referring now to FIG. 15, an exemplary embodiment of supplement plangenerator module 138 is illustrated. In an embodiment, supplement plangenerator module 138 may be configured to generate a supplementinstruction set 140 associated with the user as a function of thecomprehensive instruction set. Supplement plan generator module 138 mayinclude supplement instruction descriptor 1500. Supplement instructiondescriptor 1500 may include information that may be utilized to generatesupplement instruction set. Supplement instruction descriptor 1500 mayinclude a list of possible available supplements based on for examplediagnostic output, biological extraction, user data, and/or advisordata. Supplement instruction descriptor 1500 may include informationpertinent to a user describing user how to administer a supplementand/or recommended doses that may be necessary. Supplement instructiondescriptor 1500 may include for example, storage instructions as to asupplement. For example, a supplement containing whey protein powder mayneed to be kept in a refrigerator while a supplement such as garlic leafextract may be kept at room temperature. Supplement instructiondescriptor may include contraindication information such as supplementsthat should not be taken together such as fish oil and gingko.Supplement plan generator module 138 may use machine-learning such as bysupplement instruction descriptor learner 1504 to generate supplementinstruction descriptor and/or supplement instruction set as described inmore detail below in reference to FIG. 16. Supplement instructionlearner 1504 may generate algorithms to generate for example, a thirdmachine learning model 1508 as described in more detail below inreference to FIG. 16. Supplement plan generator module 138 may utilizesupervised and/or unsupervised machine-learning processes as describedabove in reference to FIG. 1. Supplement plan generator module 138 mayutilize lazy learning processes as descried above in reference toFIG. 1. With continued reference to FIG. 15, supplement instructiondescriptor 1500 may consult with supplement instruction descriptordatabase 1512 as described in more detail below in reference to FIG. 17.Supplement instruction descriptor database 1512 may contain informationmay include information pertaining to a supplement contained withinsupplement instruction set. With continued reference to FIG. 15,supplement plan generator module 138 may include narrative language unit912, which may be configured to determine at least an element ofnarrative language associated with at least a supplement instructiondescriptor 1500 and include the element of narrative language insupplement instruction set 140. Narrative language unit 912 mayimplement this, without limitation, by using a language processingmodule 114 to detect one or more associations between supplementinstruction descriptor 1500, or list of supplement instructiondescriptors 1500 and phrases and/or statements of narrative language.Alternatively or additionally, narrative language unit 912 may retrieveone or more elements of narrative language from a narrative languagedatabase 916, which may contain one or more tables associatingsupplement instruction descriptor 1500 and/or groups of supplementinstruction descriptors 1500 with words, sentences, and/or phrases ofnarrative language. One or more elements of narrative language may beincluded in supplement instruction set 140, for instance for display toa user as text describing a current supplement instruction set.Supplement instruction descriptor 1500 may include one or more imagessuch as an image of supplement bottle and/or the actual supplementitself; one or more images may be retrieved by supplement plan generatormodule 138 from an image database 920, which may contain one or moretables associating supplement instruction descriptor 1500, supplementinstruction set 140, or the like with one or more images.

With continued reference to FIG. 15, supplement plan generator module138 includes label synthesizer 904. Label synthesizer 904 may groupsupplement instruction descriptors 1500 according to one or moreclassification systems relating the supplement instruction descriptors1500 to each other. For example, supplement plan generator module 138and/or label synthesizer 904 may be configured to determine that a firstsupplement instruction descriptor 1500 and a second supplementinstruction descriptor 1500 belong to a shared category. A sharedcategory may be a category of supplements or type of supplement that mayto which a first supplement instruction descriptor 1500 and a secondsupplement instruction descriptor 1500 belong. For example, a firstsupplement instruction descriptor 1500 such as “store at roomtemperature” may belong to supplements including pea protein powder,oregano oil and peppermint oil. A second supplement instructiondescriptor 1500 such as “take with food” may belong to oregano oil andpeppermint oil. A given supplement instruction descriptor 1500 maybelong to a plurality of supplements.

With continued reference to FIG. 15, plan generator module 138 mayinclude user database 936. User database 936 may include any informationas described above in reference to FIGS. 9-14.

Referring now to FIG. 16, an exemplary embodiment of supplementinstruction descriptor learner 1504 is illustrated. Supplementinstruction descriptor learner 1504 may be configured to perform one ormore supervised learning processes, as described above; supervisedlearning processes may be performed by a supervised learning module 904operating on the at least a server 102 and/or on another computer devicein communication with server 102, which may include any hardware orsoftware module. For example, supervised learning algorithm may usebiological extractions and/or nutritional instructions as inputs, andsupplement instruction sets as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweenbiological extraction and supplement instruction sets; scoring functionmay for instance, seek to maximize the probability that a givenbiological extraction is associated with a supplement instruction set.In yet another non-limiting example, supervised learning algorithm mayuse diagnostic output as input and supplement instruction set as outputand a scoring function representing a desired form of relationship to bedetected between diagnostic output and supplement instruction set;scoring function may, for instance seek to maximize the probability thata given diagnostic output is associated with a supplement instructionset. In an embodiment, one or more supervised machine-learningalgorithms may be restricted to a particular domain; for instance, asupervised machine-learning process may be performed with respect to agiven set of parameters and/or categories of supplement instruction setsthat have been suspected to be related to a given set of diagnosticoutputs for instance because of a hypothesized or suspected link to afield of actions or group of actions. For example, a particular set ofdiagnostic outputs such as asthma, chronic peptic ulcer, tuberculosis,rheumatoid arthritis, ulcerative colitis, Crohn's disease, sinusitis,atherosclerosis, and hepatitis, may all relate to inflammatoryconditions, and a supervised machine-learning process may be performedto relate these diagnostic outputs to those contained within asupplement instruction set.

With continued reference to FIG. 16, supplement instruction descriptorlearner 1504 may perform one or more unsupervised machine-learningprocesses as described above, unsupervised processes may be performed byan unsupervised learning module 908 executing on the at least a server102 and/or on another computing device in communication with server 102,which may include any hardware or software module. For instance andwithout limitation, supplement instruction descriptor learner 1504 mayperform an unsupervised machine learning process on second training set116, which may cluster data of second training set 116 according todetected relationships between elements of the second training set 116,including for example relationships between diagnostic outputs and/orbiological extractions and supplement instruction sets; such informationmay then be combined with supervised machine learning results to add newcriteria for supplement instruction descriptor learner 1504 to apply inrelating diagnostic outputs and/or biological extractions to supplementinstruction sets.

With continued reference to FIG. 16, supplement instruction descriptorlearner 1504 may be configured to perform a lazy learning process as afunction of first t raining set 106, and/or second training set 116 toexamine relationships between biological extractions, diagnostic outputsand/or supplement instruction sets Lazy learning process may include anylazy learning process as described above regarding prognostic labellearner 126. Lazy learning processes may be performed by a lazy learningmodule 912 operating on the at least a server 102 and/or on anothercomputing device in communication with the at least a server 102, whichmay include any hardware or software module.

With continued reference to FIG. 16, supplement instruction descriptorlearner 1504 may generate supervised, unsupervised, and/or lazy learningprocess algorithms using data collected from users and/or informedadvisors. Supplement instruction descriptor learner 1504 may utilizeuser data 1600, which may include any of the user data as describedabove in reference to FIG. 1. This may include information describingfor example dosage form preference, dosage frequency preference, and/orquality preference. Supplement instruction descriptor learner 1504 mayutilize advisor data 1604, which may include any of the advisor data asdescribed above in reference to FIG. 1. This may include informationincluding a contraindication and/or recommendation for supplementationby an informed advisor. In an embodiment, supplement instructiondescriptor learner 1504 may utilize information obtained based onprevious interactions with a user and/or informed advisor. In anembodiment, supplement instruction descriptor learner 1504 may include afeedback mechanism, whereby new user data and/or new advisor data isutilized to update algorithms.

Referring now to FIG. 17, an exemplary embodiment of supplementinstruction descriptor database 1512 is illustrated. Supplementinstruction descriptor database 1512 may be implemented as any databaseand/or datastore suitable for use as described above. Supplementinstruction descriptor database 1512 may contain information that may beutilized to generate supplement instruction set 140 and may includeinformation pertaining to a supplement contained within supplementinstruction set. One or more database tables in supplement instructiondescriptor database 1512 may include supplement dose table 1700,supplement dose table 1700 may include information pertaining to typicaldosing ranges of a supplement. In an embodiment, doses may be organizedinto categories such as recommended doses based on condition supplementis being utilized for, or recommended doses based on age such as dosefor a child versus an adult. For example, a fish oil supplement forjoint inflammation may be dosed at two grams per day while a fish oilsupplement for high triglycerides may be dosed at nine grams per day.One or more database tables in supplement instruction descriptordatabase 1512 may include supplement administration table 1704,supplement administration table 1704 may include information pertainingto administration of a supplement. In an embodiment, supplementadministration table 1704 may include information such as how frequentlya supplement may be administered such as once per day or three times perday, as well as best times of day to take a supplement and how asupplement should be taken. For example, supplement administration table1704 may include information pertaining to Melatonin controlled releaseto be administered once at night before bed, while melatonin immediaterelease may be administered once at night before bed and once moreduring the night upon waking if necessary. One or more database tablesin supplement instruction descriptor database 1512 may includesupplement contraindication table 1708, supplement contraindicationtable 1708 may include information pertaining to contraindications thatmay occur upon taking a supplement. Contraindication table 1708 mayinclude information such as lifestyle contraindications, foodcontraindications, as well as other supplement contraindications thatmay occur while taking a supplement. For example, contraindication table1708 may include information for a user taking St. John's wort that itmay cause increase sun sensitivity and precautions such as applicationof sunscreen before sun exposure as well as avoidance of directsunlight. In yet another non-limiting example, contraindication table1708 may include information for a user taking red rice yeast extract toavoid grapefruit and grapefruit juice because of potential interactionsdue inducement of shared liver enzymes. One or more database tables insupplement instruction descriptor database 1512 may include supplementfood table 1712, supplement food table 1712 may include informationpertaining to foods that should be avoided in combination with a certainsupplement. For example, calcium supplements should be avoided beingconsumed with calcium containing beverages such as milk because in dosesabove 400 mg excess calcium is not absorbed. In yet another non-limitingexample, a supplement such as selenium may be best administered withoutfood on an empty stomach for maximum absorption. One or more databasetables in supplement instruction descriptor database 1512 may includesupplement side effect table 1716, supplement side effect table 1716 mayinclude information pertaining to potential side effects a user mayexperience while taking a certain supplement. For example, a supplementsuch as Vitamin C may initially cause loose stools and cramping while asupplement such as fish oil may cause a fishy after taste in a user'smouth. One or more database tables in supplement instruction descriptordatabase 1512 may include supplement storage table 1720, supplementstorage table 1720 may include information pertaining to optimal storageconditions for a particular supplement. For example, a supplementcontaining a probiotic strain such as Lactobacillus Acidophilus may bebest stored in a refrigerator while a supplement such as fish oil may bestored in the refrigerator or at room temperature.

Referring now to FIG. 18, an exemplary embodiment of an advisory module146 is illustrated. Advisory module 146 may be configured to generate anadvisor instruction set 1600 as a function of the diagnostic output.Advisory instruction set 1800 may contain any element suitable forinclusion in comprehensive instruction set 140; advisory instruction set1800 and/or any element thereof may be generated using any processsuitable for generation of comprehensive instruction set 140. Advisoryinstruction set 1800 may include one or more specialized instructions;specialized instructions, as used herein, are instructions the contentsof which are selected for display to a particular informed advisor.Selection of instructions for a particular informed advisor may beobtained, without limitation, from information concerning the particularinformed advisor, which may be retrieved from a user database 936 or thelike. As a non-limiting example, where an informed advisor is a doctor,specialized instruction 1804 may include data from biological extractionas described above; specialized instruction may include one or moremedical records of user, which may, as a non-limiting example, bedownloaded or otherwise received from an external database containingmedical records and/or a database (not shown) operating on at least aserver 102. As a further non-limiting example medical data relevant tofitness, such as orthopedic reports, may be provided to an informedadvisor whose role is as a fitness instructor, coach, or the like.

In an embodiment, and continuing to refer to FIG. 18, advisory module146 may be configured to receive at least an advisory input from theadvisor client device 148. At least an advisory input may include anyinformation provided by an informed advisor via advisor client device148. Advisory input may include medical information and/or advice.Advisory input may include user data, including user habits,preferences, religious affiliations, constitutional restrictions, or thelike. Advisory input may include spiritual and/or religious advice.Advisory input may include user-specific diagnostic information.Advisory input may be provided to user client device 144; alternativelyor additionally, advisory input may be fed back into system 100,including without limitation insertion into user database 936, inclusionin or use to update diagnostic engine 104, for instance by augmentingmachine-learning models and/or modifying machine-learning outputs via alazy-learning protocol or the like as described above.

With continued reference to FIG. 18, advisory module 146 may include anartificial intelligence advisor 1808 configured to perform a usertextual conversation with the user client device 144. Artificialintelligence advisor 1808 may provide output to advisor client device148 and/or user client device 144. Artificial intelligence advisor 1808may receive inputs from advisor client device 148 and/or user clientdevice 144. Inputs and/or outputs may be exchanged using messagingservices and/or protocols, including without limitation any instantmessaging protocols. Persons skilled in the art, up reviewing theentirety of this disclosure, will be aware of a multiplicity ofcommunication protocols that may be employed to exchange text messagesas described herein. Text messages may be provided in textual formand/or as audio files using, without limitation, speech-to-text and/ortext-to-speech algorithms.

With continued reference to FIG. 18, advisory module 146 may output,with advisory output, a textual entry field 1812. Textual entry field1812 may include a searchable input field that allows entry of a searchterm such as a word or phrase to be entered by a user such as aninformed advisor. In an embodiment, textual entry field 1812 may allowfor entry of a search term to be matched with labels contained withinthe at least at diagnostic output. For example, an informed advisor suchas a medical professional may enter into a search term a results of afasting glucose test after receiving at least a diagnostic output ofdiabetes. In such an instance, user such as an informed advisor may beable to search multiple results such as fasting glucose test levelsrecorded over a certain period of time such as several years and/ormonths. In yet another non-limiting example, an informed advisor such asa fitness professional may search for user's most recent exercise logand/or nutrition records. In yet another non-limiting example, aninformed advisor such as a nurse practitioner may enter information intotextual entry field 1812 to search for information pertaining to user'smedication history after receiving at least a diagnostic output of acutekidney injury. In an embodiment, textual entry field 1812 may allow auser such as an informed advisor to navigate different areas of advisoryoutput. For example, an informed advisor may utilize textual entry field1812 to navigate to different locations such as a table of contents, andor sections organized into different categories as described in moredetail below.

With continued reference to FIG. 18, advisory module 146 may include inan advisory output a category field 1816. Category field 1816 mayinclude a textual field that contains advisory output organized intocategories. Category, as used herein, is any breakdown of advisoryoutput by shared characteristics. Categories may include for example,breakdown by informed advisor type. For example, informed advisors maybe categorized into categories of expertise such as spiritualprofessionals, nutrition professionals, fitness professionals and thelike. Categories may include sub-categories of specialties such as forexample functional medicine informed advisors may be organized intosub-categories based on body system they may be treating. This couldinclude sub-categories such as dermatology specialists, Genito-urologyspecialists, gastroenterology specialists, neurology specialists and thelike. Categories may include a breakdown by time such as chronologicalorder and/or reverse chronological order. Categories may be modifiedand/or organized into test results such as for example all completeblood counts that a user has ever had performed may be located in onecategory, and all CT scans that a user has had performed may be locatedin another category. Categories may include a breakdown by relevance,such as highly relevant test results and/or test results that areoutside normal limits.

With continued reference to FIG. 18, advisory module 146 may include inan advisory output a relevance field 1820. Relevance field 1820 as usedherein is a textual field that contains advisory output informationlabeled as being relevant. Relevance, as used herein, is any informationcontained within advisory output that is closely connected and/orrelated to diagnostic output. Relevance may include information thatwould be of interest to a particular category of informed advisor. Forexample, an informed advisor such as an ophthalmologist may deeminformation contained within at least an advisory output such as ameasurement of a user's intra-ocular pressure to be of relevance, whilean advisory output containing information summarizing a user's lastappointment with a podiatrist to not be of relevance. In yet anothernon-limiting example, an informed advisor such as a fitness professionalmay deem information contained within an advisory output such as asummary of a user's last appointment with an orthopedic doctor to berelevant while a summary of a user's last colonoscopy may not berelevant. In an embodiment, relevance may be viewed on a continuum.Information contained within at least an advisory output that directlyrelates to an informed advisor and is of high probative value to aninformed advisor may be highly relevant. For example, a nutritionist maydeem a journal of a user's eating habits as highly relevant. In yetanother non-limiting example, a spiritual professional may deem asummary of a user's church patterns as highly relevant. Information thatis related to an informed advisor but does not directly affect aninformed advisor may be moderately relevant. For example, adermatologist may deem information pertaining to a user's last physicalexam with an internal medicine doctor to be moderately relevant. In yetanother non-limiting example, an endocrinologist may deem informationpertaining to a user's last appointment with a podiatrist to bemoderately relevant for a user diagnosed with diabetes. Information thatis not related to an informed advisor and does not affect an informedadvisor may be of low relevance. For example, a trauma surgeon may deeminformation about a user's last dental cleaning to be of low relevance.In yet another non-limiting example, a cardiologist may deem informationabout a user's last bone density scan to be of low relevance. In anembodiment, user such as informed advisor may use textual entry field1812 to navigate advisory output to find information that is relevant.In an embodiment, information contained within at least an advisoryoutput may be marked as relevant such as by another informed advisor.For example, a functional medicine doctor may mark an elevated fastingblood glucose level as relevant before transmitting such a result to anutrition professional.

In an embodiment, and still referring to FIG. 18, a relevance field 1820may include an image, link, or other visual element that an informedadvisor may select or otherwise interact with to expand or contract aportion of advisory output; for instance, relevance field 1820 mayinclude a symbol next to or on a section heading that can cause acorresponding section of text to display when activated a first time anddisappear when activated a second time. As a result, an informed advisormay be presented initially with some text visible and other text notvisible; initial presentation may hide all text but section headers.Alternatively or additionally, where informed advisor belongs to aparticular category of informed advisor and/or has a profile in, forinstance, advisory database 1824 indicating categories of interest tothe informed advisor, sections of text and/or images related to suchcategories may initially display while other sections do not displayunless a relevance field 1820 corresponding to such sections is selectedby the informed advisor.

With continued reference to FIG. 18, advisory module 146 containsadvisory database 1824. Advisory database 1824 may be implemented as anydatabase and/or datastore suitable for use as an advisory database. Anexemplary embodiment of an advisory database 1824 is provided below inFIG. 20.

Referring now to FIG. 19, an exemplary embodiment of an artificialintelligence advisor 1908 is illustrated. Artificial intelligenceadvisor 1908 may include a user communication learner 1900. Usercommunication learner 1900 may be any form of machine-learning learneras described above, implementing any form of language processing and/ormachine learning. In an embodiment, user communication learner 1900 mayinclude a general learner 1904; general learner 1904 may be a learnerthat derives relationships between user inputs and correct outputs usinga training set that includes, without limitation, a corpus of previousconversations. Corpus of previous conversations may be logged by atleast a server 102 as conversations take place; user feedback, and/orone or more functions indicating degree of success of a conversation maybe used to differentiate between positive input-output pairs to use fortraining and negative input-output pairs not to use for training.Outputs may include textual strings and/or outputs from any databases,modules, and/or learners as described in this disclosure, includingwithout limitation prognostic labels, prognostic descriptors,ameliorative labels, ameliorative descriptors, user information, or thelike; for instance, general learner 1904 may determine that some inputsoptimally map to textual response outputs, while other inputs map tooutputs created by retrieval of module and/or database outputs, such asretrieval of prognostic descriptors, ameliorative descriptors, or thelike. User communication learner may include a user-specific learner1908, which may generate one or more modules that learn input-outputpairs pertaining to communication with a particular user; a userspecific learner 1708 may initially use input-output pairs establishedby general learner 1904 and may modify such pairs to match optimalconversation with the particular user by iteratively minimizing an errorfunction.

Still referring to FIG. 19, general learner 1904 and/or user-specificlearner 1908 may initialize, prior to training, using one or more recordretrieved from a default response database 1912. Default responsedatabase 1912 may link inputs to outputs according to initialrelationships entered by users, including without limitation experts asdescribed above, and/or as created by a previous instance or version ofgeneral learner 1904 and/or user-specific learner 1908. Default responsedatabase 1912 may periodically be updated with information from newlygenerated instances of general learner 1904 and/or user-specific learner1908. Inputs received by artificial intelligence advisor 1908 may bemapped to canonical and/or representative inputs by synonym detection asperformed, for instance, by a language processing module 114; languageprocessing module 114 may be involved in textual analysis and/orgeneration of text at any other point in machine-learning and/orcommunication processes undergone by artificial intelligence advisor1908.

Referring now to FIG. 20, an exemplary embodiment of advisory database1824 is illustrated. One or more database tables in advisory database1824 may link to data surrounding an informed advisor. Advisory database1824 may include one or more database tables categorized by expertise ofinformed advisor. One or more database tables in advisory database 1824may include, without limitation, an artificial intelligence informedadvisors table 2004, which may contain any and all informationpertaining to artificial intelligence informed advisors. One or moredatabase tables in advisory database 1824 may include, withoutlimitation, a spiritual professional informed advisors table 2008, whichmay contain any and all information pertaining to spiritual professionalinformed advisors. Spiritual professional informed advisors may includespiritual professionals who may participate in cultivating spiritualitythrough exercise of practices such as prayer, meditation, breath work,energy work, and the like. One or more database tables in advisorydatabase 1824 may include, without limitation, a nutrition professionalinformed advisors table 2012, which may include any and all informationpertaining to nutritional informed advisors. Nutritional informedadvisors may include dieticians, chefs, and nutritionists who may offerexpertise around a user's diet and nutrition state and supplementation.One or more database tables in advisory database 1824 may include,without limitation a fitness professional informed advisors table 2016,which may include any and all information pertaining to fitnessprofessional informed advisors. Fitness professional informed advisorsmay examine the fitness state of a user and may include personaltrainers, coaches, group exercise instructors, and the like. One or moredatabase tables in advisory database 1824 may include, withoutlimitation a functional medicine informed advisors table 2020, which mayinclude any and all information pertaining to functional medicineinformed advisors. Functional medicine informed advisors may includedoctors, nurses, physician assistants, nurse practitioners and othermembers of the health care team. One or more database tables in advisorydatabase 1824 may include, without limitation a friends and familyinformed advisors table 2024, which may include any and all informationpertaining to friends and family informed advisors. Friends and familyinformed advisors may include friends and family members of a user whomay create a positive community of support for a user. One or moredatabase tables in advisory database 1824 may include, withoutlimitation an electronic behavior coach informed advisor table 2028,which may include any and all information pertaining to electronicbehavior coach informed advisors. Electronic behavior coach informedadvisors may assist a user in achieving certain results such asmodifying behaviors to achieve a result such as assisting in additionrecovery and/or changing a user's eating habits to lose weight. One ormore database tables in advisory database 1824 may include withoutlimitation a miscellaneous informed advisor table 2032, which mayinclude any and all information pertaining to miscellaneous informedadvisors. Miscellaneous informed advisors may include any informedadvisors who do not fit into one of the categories such as for exampleinsurance coverage informed advisors. Miscellaneous informed advisortable 2032 may also contain miscellaneous information pertaining toinformed advisors such as a user's preference for informed advisors in acertain geographical location and/or other preferences for informedadvisors.

Referring now to FIG. 21, an exemplary embodiment of a default responsedatabase 1912 is illustrated. Default response database 1912 may beimplemented as any database and/or datastore suitable for use asdescribed above. One or more database tables in default responsedatabase 1912 may include, without limitation, an input/output table2100, which may link default inputs to default outputs. Default responsedatabase 1912 may include a user table 2104, which may, for instance,map users and/or a user client device 180 to particular user-specificlearners and/or past conversations. Default response database 1912 mayinclude a user preference table 2108 listing preferred modes of address,turns of phrase, or other user-specific communication preferences.Default response database 1912 may include a general preference table2112, which may track, for instance, output-input pairings associatedwith greater degrees of user satisfaction.

Referring again to FIG. 19, artificial intelligence advisor may includea consultation initiator 1916 configured to detect a consultation eventin a user textual conversation and initiate a consultation with aninformed advisor as a function of the consultation event. A consultationevent, as used herein, is a situation where an informed advisor isneeded to address a user's situation or concerns, such as when a usershould be consulting with a doctor regarding an apparent medicalemergency or new condition, or with an advisor who can lend emotionalsupport when particularly distraught. Detection may be performed,without limitation, by matching an input and/or set of inputs to anoutput that constitutes an action of initiating a consultation; such apairing of an input and/or input set may be learned using a machinelearning process, for instance via general learner and/or user specificlearner 1908. In the latter case, information concerning a particularuser's physical or emotional needs or condition may be a part of thetraining set used to generate the input/input set to consultation eventpairing; for instance, a user with a history of heart disease maytrigger consultation events upon any inputs describing shortness ofbreath, chest discomfort, arrhythmia, or the like. Initiation ofconsultation may include transmitting a message to an advisor clientdevice 148 associated with an appropriate informed advisor, such aswithout limitation transmission of information regarding a potentialmedical emergency to a doctor able to assist in treating the emergency.Initiation of consultation may alternatively or additionally includeproviding an output to the user informing the user that a consultationwith an informed advisor, who may be specified by name or role, isadvisable.

Referring now to FIG. 22, an exemplary embodiment of a method 2200 ofgenerating a supplement instruction set using artificial intelligence isillustrated. At step 2205 at least a server receives training data.Receiving training data includes receiving a first training setincluding a plurality of first data entries, each first data entry ofthe plurality of first data entries including at least an element ofphysiological state data and at least a correlated first prognosticlabel. Receiving training data includes receiving a second training setincluding a plurality of second data entries, each second data entry ofthe plurality of second data entries including at least a secondprognostic label and at least a correlated ameliorative process label.Receiving training data may include any of the training data asdescribed above in reference to FIGS. 1-22.

With continued reference to FIG. 22, at step 2210 the at least a serverrecords at least a biological extraction from a user. Biologicalextraction may include any of the biological extractions as describedabove in reference to FIGS. 1-22. Recording biological extraction may beperformed by an of the methodologies as described above in reference toFIGS. 1-22.

With continued reference to FIG. 22, at step 2215 the at least a servergenerates a diagnostic output based on the at least a biologicalextraction and training data. Generating the diagnostic output includesperforming at least a machine-learning algorithm as a function of thetraining data and the at least a biological extraction. Machine-learningalgorithm may include any of the machine-learning algorithms asdescribed above in reference to FIGS. 1-22.

With continued reference to FIG. 22, at step 2220 the at least a servergenerates a comprehensive instruction set associated with the user as afunction of the diagnostic output. Comprehensive instruction set mayinclude any of the comprehensive instruction sets as described above inreference to FIGS. 1-22. In an embodiment, generating a comprehensiveinstruction set may include generating at least a nutrition set andgenerating the supplement instruction set as a function of the nutritioninstruction set. Nutrition instruction set may include any of thenutrition instruction sets as described above in reference to FIG. 1 andFIG. 9. In an embodiment, information contained within nutritioninstruction set may inform generation of the supplement instruction setas described above in more detail in reference to FIG. 1. For example,nutrition instruction set that includes a recommendation to a user toconsume magnesium rich foods such as tofu, almonds, and spinach for atleast two meals each day may be utilized to generate a supplementinstruction set that contains a magnesium dose that is appropriate basedon dietary intake if needed at all, such as when consumption throughdiet as described in nutrition instruction set may be sufficient. In anembodiment, nutrition instruction set that includes a recommendationsuch as consuming calcium rich foods including sardines, figs, yogurt,and kale may be utilized to generate a supplement instruction set thatcontains Vitamin D, to aid in calcium absorption.

With continued reference to FIG. 22, at step 2225 the at least a servergenerates a supplement instruction set as a function of thecomprehensive instruction set. Supplement instruction set may includeany of the supplement instruction sets as described above in referenceto FIGS. 1-22. In an embodiment, the at least a server may generate anutrition instruction set as a function of the supplement instructionset. This may be done by any of the methodologies as described above inreference to FIG. 1. Information contained within a supplementinstruction set may be utilized to inform a nutrition instruction set.For example, a supplement instruction set that includes a recommendationfor a user to supplement with red rice yeast extract may be utilized togenerate a nutrition instruction set that includes a recommendation toconsume foods rich in Coenzyme Q10 including organ meats, pork, chicken,trout, mackerel, sardines, oranges, cauliflower, and broccoli. In yetanother non-limiting example, a supplement instruction set that includesa recommendation to supplement with high doses of fish oil such as forhigh triglycerides may be utilized to generate a nutrition instructionset that includes a recommendation to limit and/or consume specificquantities of foods rich in Vitamin K such as kale, turnip greens,collard greens, broccoli, and edamame.

With continued reference to FIG. 22, supplement instruction set may begenerated by receiving at least an element of user data from a userclient device. User client device may include any of the user clientdevices as described above in FIG. 1. User data may include any of theuser data as described above in reference to FIG. 1, including forexample, a user preference for a particular dosage form of a supplement,a user preference for a particular dosage frequency of a supplement, auser preference for a particular quality of a supplement. For example, auser may preferer a supplement that is available as an orallydisintegrating tablet due to difficulties swallowing a pill. In yetanother non-limiting example, a user may prefer a supplement that ismanufactured under certain conditions or that has been granted certainseals of approval and/or quality assurance seals by governing agencies.

With continued reference to FIG. 22, supplement instruction set may begenerated by receiving at least an element of advisory data from anadvisor client device. Advisor client device may include any of theadvisor client devices as described above in reference to FIG. 1.Advisory data may include any of the advisory data as described above inreference to FIG. 1, including for example, a contraindication. Advisorydata may be generated by an informed advisor, which may include any ofthe informed advisors as described above in reference to FIGS. 1-22.Contraindication may include any information as to why a user should notconsume a particular supplement as described in more detail in referenceto FIG. 1. For example, an informed advisor such as a nutritionist maygenerate advisory data that a user should not consume a probioticcontaining Saccharomyces Boulardii for a user with an active yeastinfection. In yet another non-limiting example, an informed advisor suchas a health coach may generate advisory data such as a recommendationfor a user with an allergy to pineapple to not consume a bromelainsupplement as it is sourced from pineapples. In an embodiment, advisorydata may include information as to what supplements a user may want toconsider consuming. For example, an informed advisor such as afunctional nutritionist may generate advisory data to suggest a userwith hypothyroidism to consume a supplement containing iodine.

With continued reference to FIG. 22, supplement instruction set may betransmitted to a user client device. User client device may include anyof the user client devices as described above in reference to FIG. 1.Transmission may occur utilizing any of the methodologies as describedherein.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 23 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 2300 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 2300 includes a processor 2304 and a memory2308 that communicate with each other, and with other components, via abus 2312. Bus 2312 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Memory 2308 may include various components (e.g., machine-readablemedia) including, but not limited to, a random access memory component,a read only component, and any combinations thereof. In one example, abasic input/output system 2316 (BIOS), including basic routines thathelp to transfer information between elements within computer system2300, such as during start-up, may be stored in memory 2308. Memory 2308may also include (e.g., stored on one or more machine-readable media)instructions (e.g., software) 2320 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 2308 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 2300 may also include a storage device 2324. Examples ofa storage device (e.g., storage device 2324) include, but are notlimited to, a hard disk drive, a magnetic disk drive, an optical discdrive in combination with an optical medium, a solid-state memorydevice, and any combinations thereof. Storage device 2324 may beconnected to bus 2312 by an appropriate interface (not shown). Exampleinterfaces include, but are not limited to, SCSI, advanced technologyattachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394(FIREWIRE), and any combinations thereof. In one example, storage device2324 (or one or more components thereof) may be removably interfacedwith computer system 2300 (e.g., via an external port connector (notshown)). Particularly, storage device 2324 and an associatedmachine-readable medium 2328 may provide nonvolatile and/or volatilestorage of machine-readable instructions, data structures, programmodules, and/or other data for computer system 2300. In one example,software 2320 may reside, completely or partially, withinmachine-readable medium 2328. In another example, software 2320 mayreside, completely or partially, within processor 2304.

Computer system 2300 may also include an input device 2332. In oneexample, a user of computer system 2300 may enter commands and/or otherinformation into computer system 2300 via input device 2332. Examples ofan input device 2332 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 2332may be interfaced to bus 2312 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 2312, and any combinations thereof. Input device 2332may include a touch screen interface that may be a part of or separatefrom display 2336, discussed further below. Input device 2332 may beutilized as a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 2300 via storage device 2324 (e.g., a removable disk drive, aflash drive, etc.) and/or network interface device 2340. A networkinterface device, such as network interface device 2340, may be utilizedfor connecting computer system 2300 to one or more of a variety ofnetworks, such as network 2344, and one or more remote devices 2348connected thereto. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A network,such as network 2344, may employ a wired and/or a wireless mode ofcommunication. In general, any network topology may be used. Information(e.g., data, software 2320, etc.) may be communicated to and/or fromcomputer system 2300 via network interface device 2340.

Computer system 2300 may further include a video display adapter 2352for communicating a displayable image to a display device, such asdisplay device 2336. Examples of a display device include, but are notlimited to, a liquid crystal display (LCD), a cathode ray tube (CRT), aplasma display, a light emitting diode (LED) display, and anycombinations thereof. Display adapter 2352 and display device 2336 maybe utilized in combination with processor 2304 to provide graphicalrepresentations of aspects of the present disclosure. In addition to adisplay device, computer system 2300 may include one or more otherperipheral output devices including, but not limited to, an audiospeaker, a printer, and any combinations thereof. Such peripheral outputdevices may be connected to bus 2312 via a peripheral interface 2356.Examples of a peripheral interface include, but are not limited to, aserial port, a USB connection, a FIREWIRE connection, a parallelconnection, and any combinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for generating a supplement instructionset using artificial intelligence, the system comprising: at least aserver; wherein the at least a server is designed and configured to:receive training data, wherein receiving the training data furthercomprises: receiving a first training set including a plurality of firstdata entries, each first data entry of the plurality of first dataentries including at least an element of physiological state data and atleast a correlated first prognostic label; and receiving a secondtraining set including a plurality of second data entries, each seconddata entry of the plurality of second data entries including at least asecond prognostic label and at least a correlated ameliorative processlabel; a diagnostic engine operating on the at least a server, whereinthe diagnostic engine is configured to: record at least a biologicalextraction from a user; and generate a diagnostic output based on the atleast a biological extraction and the training data, wherein generatingfurther comprises performing at least a machine-learning algorithmconfigured to receive prognostic labels as inputs and outputs anameliorative label as a function of the training data and the at least abiological extraction; a plan generator module operating on the at leasta server, the plan generator module designed and configured to generate,a comprehensive instruction set associated with the user as a functionof the diagnostic output, said comprehensive instruction set including anutrition instruction set identifying a food for the user to avoidconsuming; a supplement plan generator module operating on the at leasta server, the supplement plan generation module designed and configuredto generate, a supplement instruction set associated with the user as afunction of the identified food for the user to avoid consuming, whereinthe supplement plan generator module includes a supplement instructiondescriptor configured to include a storage instruction including astorage condition associated with a supplement, wherein the supplementinstruction set comprises information pertaining to administration ofthe supplement, wherein generating the supplement instruction setfurther comprises: communicating with a supplement contraindicationtable, wherein the supplement contraindication table includesinformation pertaining to contraindications associated with asupplement; and generating the supplement instruction set based on thesupplement contraindication table.
 2. The system of claim 1, wherein thesupplement plan generator module operating on the at least a server isfurther configured to receive at least an element of user data from auser client device.
 3. The system of claim 2, wherein the user datafurther comprises a dosage form preference.
 4. The system of claim 2,wherein the user data further comprises a dosage frequency preference.5. The system of claim 2, wherein the user data further comprises aquality preference.
 6. The system of claim 1, wherein the supplementplan generator module operating on the at least a server is furtherconfigured to receive at least an element of advisory data from anadvisor client device.
 7. The system of claim 1, wherein the supplementplan generator module operating on the at least a server is furtherconfigured to transmit the supplement instruction set associated withthe user to a user client device.
 8. A method of generating a supplementinstruction set using artificial intelligence, the method comprising:receiving by at least a server training data, wherein receiving thetraining data further comprises: receiving a first training setincluding a plurality of first data entries, each first data entry ofthe plurality of first data entries including at least an element ofphysiological state data and at least a correlated first prognosticlabel; and receiving a second training set including a plurality ofsecond data entries, each second data entry of the plurality of seconddata entries including at least a second prognostic label and at least acorrelated ameliorative process label; recording by the at least aserver at least a biological extraction from a user; generating by theat least a server a diagnostic output based on the at least a biologicalextraction and the training data, wherein generating further comprisesperforming at least a machine-learning algorithm configured to receiveprognostic labels as inputs and outputs an ameliorative label as afunction of the training data and the at least a biological extraction;generating by the at least a server a comprehensive instruction setassociated with the user as a function of the diagnostic output, saidcomprehensive instruction set including a nutrition instruction setidentifying a food for the user to avoid consuming; and generating bythe at least a server a supplement instruction set associated with theuser as a function of the identified food for the user to avoidconsuming, wherein the supplement plan generator module includes asupplement instruction descriptor configured to include a storageinstruction including a storage condition associated with a supplement,wherein the supplement instruction set comprises information pertainingto administration of the supplement, wherein generating the supplementinstruction set further comprises: communicating with a supplementcontraindication table, wherein the supplement contraindication tableincludes information pertaining to contraindications associated with asupplement; and generating the supplement instruction set based on thesupplement contraindication table.
 9. The method of claim 8 furthercomprising receiving at least an element of user data from a user clientdevice.
 10. The method of claim 9, wherein receiving the at least anelement of user data further comprises receiving a dosage formpreference.
 11. The method of claim 9, wherein receiving the at least anelement of user data further comprises receiving a dosage frequencypreference.
 12. The method of claim 9, wherein receiving the at least anelement of user data further comprises receiving a quality preference.13. The method of claim 8 further comprising receiving at least anelement of advisory data from an advisor client device.
 14. The methodof claim 8 further comprising transmitting the supplement instructionset associated with the user to a user client device.